LAWRENCEVILLE, Ga. – The Association of Independent Institutions (A.I.I.) announced on Monday that Grant Shorty (SO/Albuquerque, NM) of Haskell Indian Nations University (Kan.) has been named the A.I.I.'s Men's Golfer of the Week for the duration of April 10-16.

Both Women's and Men's Cross Country improved their overall standings this weekend at the bearcat Open.

Women's Cross Country Pictured, Chantel Yazzie crossing the finish line as Haskell's first Women's Cross Country runner to cross at the Haskell Invitational. 

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Last week the weather disrupted the Indians as they opened the Outdoor Season at Pittsburg State University.  Thunderstorms and lightning prevented numerous races and events from running on schedule.  For many, the meet yesterday was their opportunity to finally compete.

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Modern systems-on-a-chip (SoCs) continue to increase in complexity, adding more components and calculation power to accommodate new performance-hungry applications such as machine learning and autonomous driving.  With increased number of SoC components, such as CPUs, GPUs, accelerators and I/O devices, comes increased demand to correctly model interoperability of various components. Traditional simulation of complex systems requires accurate models of all components comprising the system and normally results in very long simulation times. A better way is to create a set of typical traffic profiles which describe behavior of system’s masters and slaves. Such profiles should be abstract to be applied to various protocols and interfaces and be portable to be applied throughout different SoC design and verification cycles.

To address the challenges outlined above, Arm has recently announced availability of the AMBA® Adaptive Traffic Profiles (AMBA ATP) specification which lays foundation of a new synthetic traffic framework. The AMBA ATP specification includes detailed information of various transaction types and timing characteristics of those transactions. The traffic profiles defined in the specification are abstract in nature and thus could be used to generate stimuli for various standard AMBA protocols and in various environments such as RTL-based simulation, FPGA prototyping and final SoC verification. The traffic profiles outlined in the specification include a set of parameters to define timing relationships between transactions as well as timing relationships within individual transactions. Even though the traffic profile represents the behavior of a single agent it could be applied either in a concurrent manner (e.g. write and read traffic profiles running in parallel) or in a sequential manner (e.g. when one traffic completes before the next one start). Moreover, when simulating a reasonably complex system, it is possible to coordinate traffic profiles generated by multiple components. While providing abstract definition of traffic profiles, the AMBA ATP specification focuses on the use of traffic profiles with an AMBA AXI interface, outlining signaling, timing relationships between different transaction phases and between different transactions. The same application principles could be used to map the abstract traffic profiles to other AMBA protocols such as AMBA5 CHI protocol.  

To facilitate adoption of the AMBA Adaptive Traffic Profiles, Cadence has recently announced availability of SystemVerilog UVM ATP Sequence Layer which automatically implements mapping of an abstract ATP traffic to AMBA protocol specific traffic, generated by Cadence AMBA Verification IP. The ATP layer is implemented as a SystemVerilog UVM virtual sequence with the sequence item including all ATP transaction parameters as defined in the specification.

Using the provided sequence infrastructure, users can write tests to define and coordinate traffic profiles for various components in the system. The ATP Layer automatically converts the abstract traffic profile into AMBA protocol-specific traffic, e.g., AMBA5 CHI protocol traffic.

 A sample code below, shows an example of a read profile translated by Cadence ACE Verification IP in ACE protocol traffic.

   `uvm_do_with(ace_atp_vseq,                                            

                       {ace_atp_vseq.agentId == agent_id;                                // ATP agent id

                        ace_atp_vseq.atpDirection == ATP_READ;                    // direction of bursts issued by virtual sequence

                        ace_atp_vseq.startAddress == start_address;                // start of address range being accessed

                        ace_atp_vseq.endAddress == end_address;                  // end of address range being accessed

                        ace_atp_vseq.atpDomain == atp_domain;                      // domain to use for transactions

                        ace_atp_vseq.addressPattern == ATP_SEQUENTIAL;  // address pattern

                        ace_atp_vseq.transactionSize == 64;                             // number of bytes in each burst

                        ace_atp_vseq.dataSize == 4;                                          // number of bytes in each transfer

                        ace_atp_vseq.rate == 150.0/(50.0);                                // requestedBandwidth / clkFrequency

                        ace_atp_vseq.start == ATP_EMPTY;                              // start condition of the ATP FIFO

                        ace_atp_vseq.full == 128;                                               // full level of the ATP FIFO

                        ace_atp_vseq.numOfTransactions == 500;                    // number of bursts issued by this sequence

                        ace_atp_vseq.ARTV == 2;                                              // sub-transaction delay

                        ace_atp_vseq.RBR == 3;                                                // sub-transaction delay

                       });

In addition to the ATP Layer for Cadence Simulation-Based AMBA Verification IP, Cadence supports the ATP functionality in Acceleration-Based AMBA Verification IP. For detailed information about ATP support in Cadence Simulation-Based and Acceleration-Based Verification IP, visit ip.cadence.com.

With more and more SoCs employing sophisticated interconnect IP to link multiple processor cores, caches, memories, and dozens of other IP functions, the designs are enabling a new generation of low-power servers and high-performance mobile devices. The complexity of the interconnects and their advanced configurability contributes to already formidable design and verification challenges which lead to the following questions:

While your interconnect subsystem might have a correct functionality, are you starving your IP functions of the bandwidth they need? Are requests from latency-critical initiators processed on time? How can you ensure that all applications will receive the desired bandwidth in steady-state and corner use-cases?

To answer these questions, Cadence recommends the Performance Verification Methodology to ensure that the system performance meets requirements at the different levels:

1. Performance characterization: The first level of verification aims to verify the path-to-path traffic measuring the performance envelope. It targets integration bugs like clock frequency, buffer sizes, and bridge configuration. It requires to analyze the latency and bandwidth of design’s critical paths.
2. Steady state workloads: The second level of verification aims to verify the master-by-master defined loads using traffic profiles. It identifies the impact on bandwidth when running multi-master traffic with various Quality-of-Service (QoS) settings. It analyzes the DDR sub-system’s efficiency, measures bandwidth and checks whether masters’ QoS requirements are met.
3. Application specific use cases: The last level of verification simulates the use-cases and reaches the application performance corner cases. It analyzes the master-requested bandwidth as well as the DDR sub-system’s efficiency and bandwidth.

Cadence has developed a set of tools to assist customers in performance validation of their SoCs. Cadence Interconnect Workbench simplifies the setup and measurement of performance and verification testbenches and makes debugging of complex system behaviors a snap. The solution works with Cadence Verification IPs and executes on the Cadence Xcelium® Enterprise Simulator or Cadence Palladium® Accellerator/Emulator, with coverage results collected and analyzed in the Cadence vManager  Metric-Driven Signoff Platform.

To verify the performance of the Steady State Workloads, Arm has just released a new AMBA Adaptive Traffic Profile (ATP) specification which describes AMBA abstract traffic attributes and defines the behavior of the different traffic profiles in the system.

With the availability of Cadence Interconnect Workbench and AMBA VIP support of ATP, early adopters of the AMBA ATP specification can begin working immediately, ensuring compliance with the standard, and achieving the fastest path to SoC performance verification closure.

For more information on the AMBA Adaptive Traffic Profile, you can visit Dimitry's blog on AMBA Adaptive Traffic Profiles: Addressing The Challenge. 

More information on Cadence Interconnect Workbench solution is available at Cadence Interconnect Solution webpage.

Thierry

Test chips are becoming more widespread and more complex at advanced process nodes as design teams utilize early silicon to diagnose problems prior to production. But this approach also is spurring questions about whether this approach is viable at 7nm and 5nm, due to the rising cost of prototyping advanced technology, such as mask tooling and wafer costs.

Semiconductor designers have long been making test chips to validate test structures, memory bit cells, larger memory blocks, and precision analog circuits like current mirrors, PLLs, temperature sensors, and high-speed I/Os. This has been done at 90nm, 65nm, 40nm, 32nm, 28nm, etc., so having test chips at 16nm, 7nm, or finer geometries should not be a surprise. Still, as costs rise, there is debate about whether those chips are over-used given advancements in tooling, or whether they should be utilized even more, with more advanced diagnostics built into them.

Modern EDA tools are very good. You can simulate and validate almost anything with certain degree of accuracy and correctness. The key to having good and accurate tools and accurate results (for simulation) is the quality of the foundry data provided. The key to having good designs (layouts) is that the DRC deck must be of high quality and accurate and must catch all the things you are not supposed to do in the layout. Most of the challenges in advanced node is in the FEOL where semiconductor physics and lithography play outsize roles. Issues that were not an issue at more mature nodes can manifest themselves as big problems at 7nm or 5nm. Process variation across the wafer and variation across a large die also present problems that were of no consequence in more mature nodes.

The real questions to be asked are as follows:

What is the role of test chips in SoC designs?

1. Do all hard IP require test chips for validation?
2. Are test chips more important at advanced nodes compared to more mature nodes?
3. Is the importance of test chip validation relative to the type of IP protocols?
4. What are the risks if I do not validate in silicon?

In complex SoC designs, there are many high-performance protocols such as LPDDR4/4x PHY, PCIe4 PHY, USB3.0 PHY, 56G/112G SerDes, etc. Each one of these IP are very complex in and by itself. If there is any chance of failure that is not detected prior to SoC (tapeout) integration, the cost of retrofit is huge. This is why the common practice is to validate each one of these complex IP in silicon before committing to use such IP in chip integration. The test chips are used to validate that the IP are properly designed and meet the functional specifications of the protocols. They are also used to validate if sufficient margins are designed into the IP to mitigate variances due to process tolerances. All high-performance hard IP go through this test chip/silicon validation process. Oftentimes, marginality is detected at this stage. In advanced nodes, it is also important to have the test chips built under different process corners. This is intended to simulate process variations in production wafers so as to maximize yields. Advanced protocols such as 112G, GDDR6, HBM2, and PCIe4 are incredibly complex and sensitive to process variations. It is almost impossible to design these circuits and try to guarantee their performance without going through the test chip route.

Besides validating performance of the IP protocols, test silicon is also used to validate robustness of ESD structures, sensitivity to latch up, and performance degradation over wide temperature ranges. All these items are more critical in advanced nodes than more mature modes. Test chips are vehicles to guarantee design integrity in bite-size chunks. It is better to deal with any potential issues in smaller blocks than to try to fix them in the final integrated SoC.

Test chips will continue to play a vital role in helping IP and SoC teams lower the risk of their designs, and assuring optimal quality and performance in the foreseeable future. They are not going away!

To read more, please visit https://semiengineering.com/test-chips-play-larger-role-at-advanced-nodes/

PCI-SIG DevCon 2019 APAC tour has come to Tokyo and Taipei this year. The focus is predominantly around the latest updates for PCIe Gen 5 which its version 1.0 specification was just released this year in May.  A series of presentations provided by PCI-SIG on the day 1 with comprehensive information covering all aspects of Gen 5 specification, including protocol, logical, electrical, compliance updates. On the day 2 (only in Taipei), several member companies shared their view on Testing, PCB analysis and Signal integrity. The exhibit is also another spotlight of this event where the member companies showcased their latest PCIe solutions.

Presentation Track (Taipei), Exhibit (Tokyo), Exhibit (Taipei) 

Cadence, as the market leading PCIe IP vendor, participated APAC tour this year with bringing in its latest PCIe IP solution offering (Gen 5/4) to the region as well as showcasing two live demo setups in the exhibit floor. One setup is the PCIe software development kit (SDK) while the other is the Interop/compliance/debug platform. Both come with the Cadence PCIe Gen 4 hardware setup and its corresponding software kit.

The SDK can be used for Device Driver Development, Firmware Development, and for pre-silicon emulation as well. It supports Xtensa and ARM processor with Linux OS and it also equip with Ethernet interface which can be used for remote debugging. It also supports PCIe stress tests for Speed change, link enable/disable, entry/exist for lower power states, …etc. 

Cadence PCIe 4.0 Software Development Kit

The “System Interop/Compliance/Debug platform” was set up to test with multiple endpoint and System platforms. This system come with integrated Cadence software for basic system debug without the need for analyzer to perform the analysis, such as LTSSM History, TS1/TS2 transmitted/received with time stamp, Link training phases, Capturing Packet errors details, Capturing PHY TX/RX internal state machine details, ...etc.

Cadence PCIe System Interop/Compliance/Debug Platform

The year 2019 is certainly a "fruitful year" for the PCIe as more Gen 4 products are now available in the market, Gen 5 v1.0 specification got officially ratified, and PCI-SIG's revealing of Gen 6 specification development. We were glad to be part of this APAC tour with the chance to further introduce Cadence’s complete and comprehensive PCIe IP solution.

See you all next year in APAC again!

More Information

For more information on Cadence's PCIe IP offerings, see our PCI Express page.

For more information on PCIe in general, and on the various PCI standards, see the PCI-SIG website.

Related Posts

• Blog: Did You “Stress Test” Yet? Essential Step to Ensure a Quality PCIe 4.0 Product
• Blog: PCIe Gen4: It’s Official, We’re Compliant
• Blog: PCIe 3.0 Still Shines While PCIe Keeps Evolving
• Blog: The PCIe 4.0 Era Continues at PCI-SIG Developers Conference 2016

Verifying lane adapter state machine in a router design is quite an involved task and needs verification from several aspects including that for its link training functionality.

The diagram below shows two lane adapters connected to each other and each going through the link training process. Each training sub-state transition is contingent on conditions for both transmission and reception of relevant ordered sets needed for a transition. Until conditions for both are satisfied an adapter cannot transition to the next training sub-state.

As deduced from the lane adapter state machine section of USB4 specification, the reception condition for the next training sub-state transition is less strict than that of the transmission condition. For ex., for LOCK1 to LOCK2 transition, the reception condition requires only two SLOS symbols in a row being detected, while the transmission condition requires at least four complete SLOS1 ordered sets to be sent.

From the above conditions in the specification, it is a possibility that a lane adapter A may detect the two SLOS or TS ordered sets, being sent by the lane adapter B on the other end, in the very beginning as soon as it starts transmitting its own SLOS or TS ordered sets. On the other hand, it is also a possibility that these SLOS or TS ordered sets are not yet detected by lane adapter A even when it has met the condition of sending minimum number of SLOS or TS ordered sets.

In such a case, lane adapter A, even though it has satisfied the transmission condition cannot transition to the next sub-state because the reception condition is not yet met. Hence lane adapter A must first wait for the required number of ordered sets to be detected by it before it can go to the next sub-state. But this wait cannot be endless as there are timeouts defined in the specification, after which the training process may be re-attempted.

This interlocked way of operation also ensures that state machine of a lane adapter does not go out of sync with that of the other lane adapter. Such type of scenarios can occur whenever lane adapter state machine transitions to the training state from other states.

Cadence has a mature Verification IP solution for the verification of various aspects of the logical layer of a USB4 router design, with verification capabilities provided to do a comprehensive verification of it.

I could barely recognize Ben Foster in 3:10 to Yuma, but I was blown away just the same by him as in his star making turn from Hostage. What makes Foster so special in Yuma?

Yuma contains two of Hollywood’s finest: Russell Crowe and Christian Bale. Bale is excellent, Crowe a little too relaxed to be cock-sure-dangerous. Both are unable to provide the powder-keg relationship that the movie demands.

Into this void steps Ben Foster. He plays Charlie Prince, sidekick to Crowe’s dangerous and celebrated outlaw Ben Wade. When Wade is captured, Prince is infuriated. He initiates an effort suffused with desperate passion to rescue his boss.

Playing Prince with a mildly effeminate gait, Foster quickly becomes the movie’s beating heart. What struck me in particular was that Foster was able to balance method acting with just plain good acting. He plays his character organically but isn’t above drawing attention with controlled staginess.

Gradually, Foster’s willingness to control a scene blend in with that of Prince’s. Is the character manipulating his circumstances in the movie or is it the actor playing a fine hand? Foster is so entertaining, the answer is immaterial.

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This new film is the latest remake of Devdas, but what is equally interesting is the fact that it is in conversation with films made in the West. Unlike Bhansali’s more spectacular version of the older story, Anurag Kashyap’s Dev.D is a genuine rewriting of Sarat Chandra’s novel. Kashyap doesn’t flinch from depicting the individual’s downward spiral, but he also gives women their own strength. He has set out to right a wrong—or, at least, tell a more realistic, even redemptive, story. If these characters have lost some of the affective depth of the original creations, they have also gained the hard edges of modern lives.

We don’t always feel the pain of Kashyap’s characters, but we are able to more readily recognize them. Take Chandramukhi, or Chanda, who is a school-girl humiliated by the MMS sex-scandal. Her father, protective and patriarchal, says that he has seen the tape and thinks she knew what she was doing. “How could you watch it?” the girl asks angrily. And then, “Did you get off on it?” When was the last time a father was asked such a question on the Hindi screen? With its frankness toward sex and masturbation, Dev.D takes a huge step toward honesty. In fact, more than the obvious tributes to Danny Boyle’s Trainspotting, or the over-extended psychedelic adventure on screen, in fact, as much as the moody style of film-making, the candour of such questions make Dev.D a film that is truly a part of world cinema.

Rave Out © 2007 IndiaUncut.com. All rights reserved.
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I loved Nina Paley’s brilliant animated film Sita Sings the Blues. If you’re reading this, stop right now—and watch the film here.

Paley has set the story of the Ramayana to the 1920s jazz vocals of Annette Hanshaw. The epic tale is interwoven with Paley’s account of her husband’s move to India from where he dumps her by e-mail. The Ramayana is presented with the tagline: “The Greatest Break-Up Story Ever Told.”

All of this should make us curious. But there are other reasons for admiring this film:

The film returns us to the message that is made clear by every village-performance of the Ramlila: the epics are for everyone. Also, there is no authoritative narration of an epic. This film is aided by three shadow puppets who, drawing upon memory and unabashedly incomplete knowledge, boldly go where only pundits and philosophers have gone before. The result is a rendition of the epic that is gloriously a part of the everyday.

This idea is taken even further. Paley says that the work came from a shared culture, and it is to a shared culture that it must return: she has put the film on Creative Commons—viewers are invited to distribute, copy, remix the film.

Of course, such art drives the purists and fundamentalists crazy. On the Channel 13 website, “Durgadevi” and “Shridhar” rant about the evil done to Hinduism. It is as if Paley had lit her tail (tale!) and set our houses on fire!

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This is the 23rd installment of The Rationalist, my column for the Times of India.

I had a strange dream last night. I dreamt that the government had passed a law that made using laptops illegal. I would have to write this column by hand. I would also have to leave my home in Mumbai to deliver it in person to my editor in Delhi. I woke up trembling and angry – and realised how Indian farmers feel every single day of their lives.

My column today is a tale of two satyagrahas. Both involve farmers, technology and the freedom of choice. One of them began this month – but first, let us go back to the turn of the millennium.

As the 1990s came to an end, cotton farmers across India were in distress. Pests known as bollworms were ravaging crops across the country. Farmers had to use increasing amounts of pesticide to keep them at bay. The costs of the pesticide and the amount of labour involved made it unviable – and often, the crops would fail anyway.

Then, technology came to the rescue. The farmers heard of Bt Cotton, a genetically modified type of cotton that kept these pests away, and was being used around the world. But they were illegal in India, even though no bad effects had ever been recorded. Well, who cares about ‘illegal’ when it is a matter of life and death?

Farmers in Gujarat got hold of Bt Cotton seeds from the black market and planted them. You’ll never guess what happened next. As 2002 began, all cotton crops in Gujarat failed – except the 10,000 hectares that had Bt Cotton. The government did not care about the failed crops. They cared about the ‘illegal’ ones. They ordered all the Bt Cotton crops to be destroyed.

It was time for a satyagraha – and not just in Gujarat. The late Sharad Joshi, leader of the Shetkari Sanghatana in Maharashtra, took around 10,000 farmers to Gujarat to stand with their fellows there. They sat in the fields of Bt Cotton and basically said, ‘Over our dead bodies.’ ¬Joshi’s point was simple: all other citizens of India have access to the latest technology from all over. They are all empowered with choice. Why should farmers be held back?

The satyagraha was successful. The ban on Bt Cotton was lifted.

There are three things I would like to point out here. One, the lifting of the ban transformed cotton farming in India. Over 90% of Indian farmers now use Bt Cotton. India has become the world’s largest producer of cotton, moving ahead of China. According to agriculture expert Ashok Gulati, India has gained US$ 67 billion in the years since from higher exports and import savings because of Bt Cotton. Most importantly, cotton farmers’ incomes have doubled.

Two, GMO crops have become standard across the world. Around 190 million hectares of GMO crops have been planted worldwide, and GMO foods are accepted in 67 countries. The humanitarian benefits have been massive: Golden Rice, a variety of rice packed with minerals and vitamins, has prevented blindness in countless new-born kids since it was introduced in the Philippines.

Three, despite the fear-mongering of some NGOs, whose existence depends on alarmism, the science behind GMO is settled. No harmful side effects have been noted in all these years, and millions of lives impacted positively. A couple of years ago, over 100 Nobel Laureates signed a petition asserting that GMO foods were safe, and blasting anti-science NGOs that stood in the way of progress. There is scientific consensus on this.

The science may be settled, but the politics is not. The government still bans some types of GMO seeds, such as Bt Brinjal, which was developed by an Indian company called Mahyco, and used successfully in Bangladesh. More crucially, a variety called HT Bt Cotton, which fights weeds, is also banned. Weeding takes up to 15% of a farmer’s time, and often makes farming unviable. Farmers across the world use this variant – 60% of global cotton crops are HT Bt. Indian farmers are so desperate for it that they choose to break the law and buy expensive seeds from the black market – but the government is cracking down. A farmer in Haryana had his crop destroyed by the government in May.

On June 10 this year, a farmer named Lalit Bahale in the Akola District of Maharashtra kicked off a satyagraha by planting banned seeds of HT Bt Cotton and Bt Brinjal. He was soon joined by thousands of farmers. Far from our urban eyes, a heroic fight has begun. Our farmers, already victimised and oppressed by a predatory government in countless ways, are fighting for their right to take charge of their lives.

As this brave struggle unfolds, I am left with a troubling question: All those satyagrahas of the past by our great freedom fighters, what were they for, if all they got us was independence and not freedom?

© 2007 IndiaUncut.com. All rights reserved.
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This is the 24th installment of The Rationalist, my column for the Times of India.

Back in the last decade, I was a cricket journalist for a few years. Then, around 12 years ago, I quit. I was jaded as hell. Every game seemed like déjà vu, nothing new, just another round on the treadmill. Although I would remember her fondly, I thought me and cricket were done.

And then I fell in love again. Cricket has changed in the last few years in glorious ways. There have been new ways of thinking about the game. There have been new ways of playing the game. Every season, new kinds of drama form, new nuances spring up into sight. This is true even of what had once seemed the dullest form of the game, one-day cricket. We are entering into a brave new world, and the team leading us there is England. No matter what happens in the World Cup final today – a single game involves a huge amount of luck – this England side are extraordinary. They are the bridge between eras, leading us into a Golden Age of Cricket.

I know that sounds hyperbolic, so let me stun you further by saying that I give the IPL credit for this. And now, having woken up you up with such a jolt on this lovely Sunday morning, let me explain.

Twenty20 cricket changed the game in two fundamental ways. Both ended up changing one-day cricket. The first was strategy.

When the first T20 games took place, teams applied an ODI template to innings-building: pinch-hit, build, slog. But this was not an optimal approach. In ODIs, teams have 11 players over 50 overs. In T20s, they have 11 players over 20 overs. The equation between resources and constraints is different. This means that the cost of a wicket goes down, and the cost of a dot ball goes up. Critically, it means that the value of aggression rises. A team need not follow the ODI template. In some instances, attacking for all 20 overs – or as I call it, ‘frontloading’ – may be optimal.

West Indies won the T20 World Cup in 2016 by doing just this, and England played similarly. And some sides began to realise was that they had been underestimating the value of aggression in one-day cricket as well.

The second fundamental way in which T20 cricket changed cricket was in terms of skills. The IPL and other leagues brought big money into the game. This changed incentives for budding cricketers. Relatively few people break into Test or ODI cricket, and play for their countries. A much wider pool can aspire to play T20 cricket – which also provides much more money. So it makes sense to spend the hundreds of hours you are in the nets honing T20 skills rather than Test match skills. Go to any nets practice, and you will find many more kids practising innovative aggressive strokes than playing the forward defensive.

As a result, batsmen today have a wider array of attacking strokes than earlier generations. Because every run counts more in T20 cricket, the standard of fielding has also shot up. And bowlers have also reacted to this by expanding their arsenal of tricks. Everyone has had to lift their game.

In one-day cricket, thus, two things have happened. One, there is better strategic understanding about the value of aggression. Two, batsmen are better equipped to act on the aggressive imperative. The game has continued to evolve.

Bowlers have reacted to this with greater aggression on their part, and this ongoing dialogue has been fascinating. The cricket writer Gideon Haigh once told me on my podcast that the 2015 World Cup featured a battle between T20 batting and Test match bowling.

This England team is the high watermark so far. Their aggression does not come from slogging. They bat with a combination of intent and skills that allows them to coast at 6-an-over, without needing to take too many risks. In normal conditions, thus, they can coast to 300 – any hitting they do beyond that is the bonus that takes them to 350 or 400. It’s a whole new level, illustrated by the fact that at one point a few days ago, they had seven consecutive scores of 300 to their name. Look at their scores over the last few years, in fact, and it is clear that this is the greatest batting side in the history of one-day cricket – by a margin.

There have been stumbles in this World Cup, but in the bigger picture, those are outliers. If England have a bad day in the final and New Zealand play their A-game, England might even lose today. But if Captain Morgan’s men play their A-game, they will coast to victory. New Zealand does not have those gears. No other team in the world does – for now.

But one day, they will all have to learn to play like this.

© 2007 IndiaUncut.com. All rights reserved.
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Physical synthesis has been around in various forms for many years. The basic idea is to bring some awareness of physical layout into synthesis. This week (June 3, 2015) Cadence is rolling out the Genus™ Synthesis Solution, a next-generation RTL synthesis tool that takes physical awareness in some new directions.

Here are four important things to know about Genus technology:

• A massively parallel architecture improves turnaround time by up to 5X while maintaining quality of results
• The Genus solution synthesizes up to 10M+ instances flat without impacting power, performance and area (PPA)
• The Genus solution provides tight correlation with the Innovus Implementation System, using the same placement and routing algorithms
• Globally focused PPA optimization saves up to 20% datapath area and power

Compared to previous-generation products such as the Cadence Encounter RTL Compiler Advanced Physical Option, the Genus solution approaches physical synthesis in a different way. The Encounter solution applied physical optimization “at the tail end of synthesis,” said David Stratman, senior principal product manager at Cadence. “We were doing a final incremental push, but we could only do so much, since we had locked in a lot of the earlier steps from a logical-only synthesis perspective.”

Genus Synthesis Solution supports the physical synthesis features in the previous Encounter solution, but it also brings the full physical scope upstream to RTL logic designers. “It’s going to enable the unit-level RTL designer to gain the benefits of physical synthesis without having to understand it,” Stratman said. As an example, users can apply generic (unmapped) placement at the earliest stages of synthesis, using a lightweight version of the Innovus placement engine. The bottom line: “Genus is a full solution where every step of synthesis can be done physically.”

Getting Massively Parallel

If you bring physical data into synthesis, you need a way to improve capacity and runtimes, especially with today’s gigantic advance-node SoCs. That’s why a massively parallel architecture is the cornerstone of the Genus solution. In this way, the Genus solution is following in the footsteps of the Innovus Implementation System, which also provides a massively parallel architecture.

Both the Innovus and Genus solutions can handle blocks of 10M instances flat. Given that SoCs today may have up to 100M instances, and often up to 50-100 top-level blocks, this is an important capability. Many tools today will only handle blocks of 1M instances. As a result, design teams often have to constrain block sizes.

Genus technology offers timing-driven, multi-level design partitioning across multiple threads and machines. It enables a near-linear runtime scaling without impacting PPA. According to Stratman, the Genus solution will scale well beyond 64 CPUs for a large design, with a “sweet spot” around 8-20 CPUs for today’s typical block sizes. Runs that used to take days, he noted, can now be done in hours.

As shown below, Genus technology leverages parallelism at three levels. The Genus solution can distribute design partitions to multiple threads or CPUs, and also supports local algorithm-level multithreading on each machine with shared memory. An adaptive scheduler ensures the best use of the available CPUs.

Fig. 1 – Genus Synthesis Solution provides three levels of parallelism

With its massive parallelism, Stratman said, Genus technology can obtain production-level quality of results (QoR) in runtimes typically seen in “prototype-level” synthesis runs. The “secret sauce,” he said, is in the partitioning. Cadence has found a way to generate partitions in a way that “slices the design more intelligently, and takes advantage of the Genus database to merge partitions without losing timing, power, or area,” Stratman said.

Playing in the Sandbox

In the Genus Synthesis Solution, a process called “sandboxing” allows any subset or partition of a design to be extracted along with full timing and a physical context. Optimization algorithms will treat a sandbox as a complete design.

The “Clipper” flow clips out or extracts the context of the larger SoC blocks. “It’s kind of a skeleton floorplan but it has all the timing information,” Stratman said. These extracted contexts include all the critical physical information to make the right RTL synthesis choices at the unit level. This information is used to streamline the handoffs between unit-level RTL designers, integration engineers, and implementation engineers. It’s a way for logic designers to gain some physical knowledge without having to be a physical synthesis expert, or without having to run a full top-level synthesis.

Fig. 2 – Clipper flow provides context for unit-level blocks

Correlation with Innovus Implementation System

Although Genus technology can work with third-party IC implementation systems, it shares algorithms and engines with Innovus Implementation System, as well as a common user interface. As shown below, both the Genus and Innovus solutions use a table-based Quantus QRC parasitic extraction, effective current source model (ECSM) and composite current source (CCS) delay calculations, and a unified global routing engine. Timing and wire length claim a 5% correlation.

Fig. 3 – Genus Synthesis Solution offers tight correlation with Innovus Implementation System

Genus technology doesn’t model everything to the same level of accuracy as the Innovus solution, however. “We chose to be lighter weight and more nimble to get expected runtimes,” Stratman said. A tight correlation is possible because the Genus and Innovus solutions use a similar code base. This correlation will be tighter than that between Encounter RTL Compiler Advanced Physical Option and the Encounter Digital Implementation System today.

Genus Synthesis Solution uses a new Hybrid Global Router that provides the ability to resolve congestion and construct layer-aware, timing-driven wire topologies. This accelerates analysis and debug, and reduces iterations. Users can avoid blockages and see a full Manhattan route as opposed to “flight lines.” Layer awareness is particularly important, given the large RC variations within the metal stack at advanced process nodes.

A version of the Innovus GigaPlace engine is available within the Genus solution. Here, users can do an RTL-level generic gate placement early in the synthesis flow (“generic gate” means there is no mapping into standard cell libraries, but there’s still an area estimate). This helps designers understand PPA tradeoffs earlier.

While users can go all the way to a design-rule “legal” placement with Genus Synthesis Solution, this isn’t generally recommended. “You can do a placement and use the same algorithms as GigaPlace and get a nice correlation without all the runtimes and additional steps of doing a fully legal placement,” Stratman said.

So where does Genus technology end and Innovus technology begin? That’s up to the user. You could use the Genus solution for logical synthesis and run all physical implementation in the Innovus system. If you run physical synthesis within the Genus solution, there’s more work earlier in the flow, but you get better insights into downstream problems and reduce iterations.

“Physical synthesis should be no more than 2X [runtime] of logic synthesis,” Stratman said. “All of the runtime that moves up should be shaved off of the place-and-route stages, because now you can do lightweight incremental optimization and incremental placement. The overall flow should be runtime neutral or better.”

Be Globally Aware

Finally, Genus Synthesis Solution offers a globally focused early PPA optimization across the whole datapath, delivering up to a 20% area reduction in the datapath. Stratman noted that this capability is a follow-on to an RCP feature called “globally focused mapping” that can determine the best cells to use in a library. What’s new with the Genus solution is that this concept has been applied at the arithmetic level.

For example, there are many ways to configure a multiplier – you may want to prioritize speed, power, or size. In the past, Stratman noted, synthesis tools have not been very good at globally optimizing the architecture selection for PPA optimization. “We can [now] find the most efficient global datapath implementation for a given region,” he said.

For further information about the Cadence Genus Synthesis Solution, including a datasheet and technical product brief, see this landing page.

Richard Goering

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There has been so much hype about the “Internet of Things” (IoT) that it is refreshing to hear about a cutting-edge development project that can bring concrete benefits to millions of people. That project is the ongoing development of the Google Smart Contact Lens, and it was detailed in a keynote speech June 8 at the Design Automation Conference (DAC 2015).

The keynote speech was given by Brian Otis (right), a director at Google and a research associate professor at the University of Washington. The “smart lens” that the project envisions is essentially a disposable contact lens that fits on an eye and continuously monitors blood glucose levels. This is valuable information for anyone who has, or may someday have, diabetes.

Since he was speaking to an engineering audience, Otis focused on the challenges behind building such a device, and described some of the strategies taken by Google and its partner, Novartis. The project required new approaches to miniaturization, low-power design, and connectivity, as well as a comfortable and reliable silicon-to-human interface. Otis discussed the “why” as well and showed how the device could potentially save or improve millions of lives.

Millions of Users

First, a bit of background. Google announced the smart lens project in a blog post in January 2014. Since then it has been featured in news outlets including Forbes, Time, and the Wall Street Journal. In March 2015, Time reported that Google has been granted a patent for a smart contact lens.

The smart lens monitors the level of blood glucose by looking at its concentration in tears. The lens includes a wireless system on chip (SoC) and a miniaturized glucose sensor. A tiny pinhole in the lens allows tear fluid to seep into the sensor, and a wireless antenna handles communications to the wireless devices.

“We figure that if we can solve a huge problem, it is probably worth doing,” Otis said. “Diabetes is one example.” He noted 382 million people worldwide have diabetes today, and that 35% of the U.S. population may be pre-diabetic. Today, diabetics must *** their fingers to test blood glucose levels, a procedure that is invasive, painful, and subject to infrequent monitoring.

According to Otis, the smart contact lens represents a “new category of wearable devices that are comfortable, inexpensive, and empowering.” The lens does sensor data logging and uses a portable instrument to measure glucose levels. It is thin, cheap, and disposable, he said.

Moreover, the lens is not just for people already diagnosed with diabetes—it’s for anyone who is pre-diabetic, or may be at risk due to genetic predisposition. “If we are pro-active rather than re-active,” Otis said, “Instead of waiting until a person has full-fledged diabetes, we could make a huge difference in peoples’ lives and lower the costs of treating them.”

Technical Challenges

No one has built anything quite like the smart lens, so researchers at Google and Novartis are treading new ground. Otis identified three key challenges:

• Miniaturization: Everything must be really small—the SoC, the passive components, the power supply. Components must be flexible and cheap, and support thin-film integration.
• Platform: Google has developed a reusable platform that includes tiny, always-on wireless sensors, ultra low-power components, and standards-based interfaces.
• Data: Researchers are looking for the best ways to get the resulting data into a mobile device and onto the cloud.

Comfort is another concern. “This is not intended to be for the most severe cases,” Otis said. “This is intended to be for all of us as a pro-active way of improving our lifestyles.”

The platform provides a bidirectional encrypted wireless link, integrated power management, on-chip memory, standards-based RFID link, flexible sensor interface, high-resolution potentiostat sensor, and decoupling capacitors. Most of these capabilities are provided by the standard CMOS SoC, which is a couple hundred microns on a side and only “tens of microns” thick.

Otis noted that unpackaged ICs are typically 250 microns thick when they come back from the foundry. Thus, post-processing is needed so the IC will fit into a contact lens.

Furthermore, the design requires precision analog circuitry and additional environmental sensors. “Some of this stuff sounds mundane but it is really hard, especially when you find out you can’t throw large decoupling capacitors and bypass capacitors onto a board, and all that has to be re-integrated into the chip,” Otis said.

Sensor Challenges

Getting information from the human body is challenging. The smart lens sensor does a direct chemical measurement on the surface of the eye. The sensor is designed to work with very low glucose concentrations. This is because the concentration of glucose in tears is an order of magnitude lower than it is in blood.

In brief, the sensor has two parallel plates that are coated with an enzyme that converts glucose into hydrogen peroxide, which flows around the electrodes of the sensor. This is actually a fairly standard way of doing glucose monitoring. However, the smart lens sensor has two electrodes compared to the typical three.

In manufacturing, it is essential to keep costs low. Otis outlined a three-step manufacturing process:

• Start with the bottom layer, and mold a contact lens in the way you typically would.
• Add the electronics package on top of that layer.
• Build a second layer that encapsulates the electronics and provides the curvature needed for comfort and vision correction.

Beyond the technical challenges are the “clinical” challenges of working with human beings. The human body “is messy and very variable,” Otis said. This variability affects sensor performance and calibration, RF/electro-magnetic performance, system reliability, and comfort.

The final step is making use of the data. “We need to get the data from the device into a phone, and then display it so users can visualize the data,” Otis said. This provides “actionable feedback” to the person who needs it. Eventually, the data will need to be stored in the cloud.

As he concluded his talk, Otis noted that the platform his group developed may have many applications beyond glucose monitoring. “There is a lot you can do with a bunch of logic and sensing capability,” he said, “and there are hundreds of biomarkers beyond glucose.” Clearly this will be an interesting technology to watch.

Richard Goering

Related Blog Post

- Gary Smith at DAC 2015: How EDA Can Expand Into New Directions

As a leading venture capitalist in the electronics technology, as well as CEO of Cadence, Lip-Bu Tan has unique insights into ongoing changes that will impact EDA providers and users. Tan shared some of those insights in a “fireside chat” with Ed Sperling, editor in chief of Semiconductor Engineering, at the Design Automation Conference (DAC 2015) on June 9.

Topics of this discussion included industry consolidation, the need for more talent and more startups, Internet of Things (IoT) opportunities and challenges, the shift from ICs to full product development, and the challenges of advanced nodes. Following are some excerpts from this conversation, held at the DAC Pavilion theater on the exhibit floor.

Ed Sperling (left) and Lip-Bu Tan (right) discuss trends in semiconductors and EDA

Q: As you look out over the semiconductor and EDA industries these days, what worries you most?

Tan: At the top of my list is all the consolidation that is going on. Secondly, chip design complexity is increasing substantially. Time-to-market pressure is growing and advanced nodes have challenges.

The other thing I worry about is that we need to have more startups. There’s a lot of innovation that needs to happen. And this industry needs more top talent. At Cadence, we have a program to recruit over 10% of new hires every year from college graduates. We need new blood and new ideas.

Q: EDA vendors were acquiring companies for many years, but now the startups are pretty much gone. Where does the next wave of innovation come from?

Tan: I’ve been an EDA CEO for the last seven years and I really enjoy it because so much innovation is needed. System providers have very big challenges and very different needs. You have to find the opportunities and go out and provide the solutions.

The opportunities are not just in basic tools. Massive parallelism is critical, and the power challenge is huge. Time to market is critical, and for the IoT companies, cost is going to be critical. If you want to take on some good engineering challenges, this is the most exciting time.

Q: You live two lives—you’re a CEO but you’re also an investor. Where are the investments going these days and where are we likely to see new startups?

Tan: Clearly everybody is chasing the IoT. There is a lot of opportunity in the cloud, in the data center. Also, I’m a big believer in video, so I back companies that are video related. A big area is automotive. ADAS [Advanced Driver Assistance Systems] is a tremendous opportunity.

These companies can help us understand how the industry is transforming, and then we can provide solutions, either in terms of IP, tools, or the PCB. Then we need to connect from the system level down to semiconductors. I think it’s a different way to design.

Q: What happens as we start moving from companies looking to design a semiconductor to system companies who are doing things from the perspective that we have this purpose for our software?

Tan: We are extending from EDA to what we call system design enablement, and we are becoming more application driven. The application at the system level will drive the silicon design. We need to help companies look at the whole system including the power envelope and signal integrity. You don’t want to be in a position where you design a chip all the way to fabrication and then find the power is too high.

We help the customers with hardware/software co-design and co-verification. We have a design suite and a verification suite that can provide customers with high-level abstractions, as well as verify IP blocks at the system level. Then we can break things down to the component level with system constraints in mind, and drive power-aware, system-aware design.

We are starting to move into vertical markets. For example, medical is a tremendous opportunity.

Q: How does this approach change what you provide to customers?

Tan: Every year I spend time meeting with customers. I think it is very important to understand what they are trying to design, and it is also important to know the customer’s customer requirements. We might say, “Wait a minute, for this design you may want to think about power or the library you’re using.” We help them understand what foundry they should use and what process they should use. They don’t view me as a vendor—they view me as a partner.

We also work very closely with our IP and foundry partners. We work as one team—the ultimate goal is customer success.

Q: Is everybody going to say, FinFETs are beautiful, we’re going to go down to 10nm or 7nm—or is it a smaller number of companies who will continue down that path?

Tan: Some of the analog/mixed-signal companies don’t need to go that far. We love those customers—we have close to 50% of that business. But we also have customers in the graphics or processor area who are really pushing the envelope, and need to be in 16nm, 14nm, or 10nm. We work very closely with those guys to make sure they can go into FinFETs.

We always want to work with the customer to make sure they have a first-time silicon success. If you have to do a re-spin, you miss the opportunity and it’s very costly.

Q: There’s a new market that is starting to explode—IoT. How real is that world to you? Everyone talks about large numbers, but is it showing up in terms of tools?

Tan: Everybody is talking about huge profits, but a lot of the time I think it is just connecting old devices that you have. Billions of units, absolutely yes, but if you look close enough the silicon percentage of that revenue is very tiny. A lot of the profit is on the service side. So you really need to look at the service killer app you are trying to provide.

What’s most important to us in the IoT market is the IP business. That’s why we bought Tensilica—it’s programmable, so you can find the killer app more quickly. The other challenges are time to market, low power, and low cost.

Q: Where is system design enablement going? Does it expand outside the traditional market for EDA?

Tan: It’s not just about tools. IP is now 11% of our revenue. At the PCB level, we acquired a company called Sigrity, and through that we are able to drive system analysis for power, signal integrity, and thermal. And then we look at some of the verticals and provide modeling all the way from the system level to the component level. We make sure that we provide a solution to the end customer, rather than something piecemeal.

Q: What do you think DAC will look like in five years?

Tan: It’s getting smaller. We need to see more startups and innovative IP solutions. I saw a few here this year, and that’s good. We need to encourage small startups.

Q: Where do we get the people to pull this off? I don’t see too many people coming into EDA.

Tan: I talk to a lot of university students, and I tell them that this small industry is a gold mine. A lot of innovation is needed. We need them to come in [to EDA] rather than join Google or Facebook. Those are great companies, but there is a lot of fundamental physical innovation we need.

Richard Goering

Related Blog Posts

- Gary Smith at DAC 2015: How EDA Can Expand Into New Directions

- DAC 2015: Google Smart Contact Lens Project Stretches Limits of IC Design

- Q&A with Nimish Modi: Going Beyond Traditional EDA

Design and verification standards are critical if we want to get a new generation of Internet of Things (IoT) devices into the market, according to panelists at an Accellera Systems Initiative breakfast at the Design Automation Conference (DAC 2015) June 9. However, IoT devices for different vertical markets pose very different challenges and requirements, making the standards picture extremely complicated.

The panel was titled “Design and Verification Standards in the Era of IoT.” It was moderated by industry editor John Blyler, CEO of JB Systems Media and Technology. Panelists were as follows, shown left to right in the photo below:

• Lu Dai, director of engineering, Qualcomm
• Wael William Diab, senior director for strategy marketing, industry development and standardization, Huawei
• Chris Rowen, CTO, IP Group, Cadence Design Systems, Inc.

In opening remarks, Blyler recalled a conversation from the recent IEEE International Microwave Symposium in which a panelist pointed to the networking and application layers as the key problem areas for RF and wireless standardization. Similarly, in the IoT space, we need to look “higher up” at the systems level and consider both software and hardware development, Blyler said.

Rowen helped set some context for the discussion by noting three important points about IoT:

• IoT is not a product segment. Vertical product segments such as automotive, medical devices, and home automation all have very different characteristics.
• IoT “devices” are components within a hierarchy of systems that includes sensors, applications, user interface, gateway application (such as cell phone), and finally the cloud, where all data is aggregated.
• A bifurcation is taking place in design. We are going from extreme scale SoCs to “extreme fit” SoCs that are specialized, low energy, and very low cost.

Here are some of the questions and answers that were addressed during the panel discussion.

Q: The claim was recently made that given the level of interaction between sensors and gateways, 50X more verification nodes would have to be checked for IoT. What standards need to be enhanced or changed to accomplish that?

Rowen: That’s a huge number of design dimensions, and the way you attack a problem of that scale is by modularization. You define areas that are protected and encapsulated by standards, and you prove that individual elements will be compliant with that interface. We will see that many interesting problems will be in the software layers.

Q: Why is standardization so important for IoT?

Dai: A company that is trying to make a lot of chips has to deal with a variety of standards. If you have to deal with hundreds of standards, it’s a big bottleneck for bringing your products to market. If you have good standardization within the development process of the IC, that helps time to market.

When I first joined Qualcomm a few years ago, there was no internal verification methodology. When we had a new hire, it took months to ramp up on our internal methodology to become effective. Then came UVM [Universal Verification Methodology], and as UVM became standard, we reduced our ramp-up time tremendously. We’ve seen good engineers ramp up within days.

Diab: When we start to look at standards, we have to do a better job of understanding how they’re all going to play with each other. I don’t think one set of standards can solve the IoT problem. Some standards can grow vertically in markets like industrial, and other standards are getting more horizontal. Security is very important and is probably one thing that goes horizontally.

Requirements for verticals may be different, but processing capability, latency, bandwidth, and messaging capability are common [horizontal] concerns. I think a lot of standards organizations this year will work on horizontal slices [of IoT].

Q: IoT interoperability is important. Any suggestions for getting that done and moving forward?

Rowen: The interoperability problem is that many of these [IoT] devices are wireless. Wireless is interesting because it is really hard – it’s not like a USB plug. Wireless lacks the infrastructure that exists today around wired standards. If we do things in a heavily wireless way, there will be major barriers to overcome.

Dai: There are different standards for 4G LTE technology for different [geographical] markets. We have to make a chip that can work for 20 or 30 wireless technologies, and the cost for that is tremendous. The U.S., Europe, and China all have different tweaks. A good standard that works across the globe would reduce the cost a lot.

Q: If we’re talking about the need to define requirements, a good example to look at is power. Certainly you have UPF [Unified Power Format] for the chip, board, and module.

Rowen: There is certainly a big role for standards about power management. But there is also a domain in which we’re woefully under-equipped, and that is the ability to accurately model the different power usage scenarios at the applications level. Too often power devolves into something that runs over thousands of cycles to confirm that you can switch between power management levels successfully. That’s important, but it tells you very little about how much power your system is going to dissipate.

Dai: There are products that claim to be UPF compliant, but my biggest problem with my most recent chip was still with UPF. These tools are not necessarily 100% UPF compliant.

One other concern I have is that I cannot get one simulator to pass my Verilog code and then go to another that will pass. Even though we have a lot of tools, there is no certification process for a language standard.

Q: When we create a standard, does there need to be a companion compliance test?

Rowen: I think compliance is important. Compliance is being able to prove that you followed what you said you would follow. It also plays into functional safety requirements, where you need to prove you adhered to the flow.

Dai: When we [Qualcomm] sell our 4G chips, we have to go through a lot of certifications. It’s often a differentiating factor.

Q: For IoT you need power management and verification that includes analog. Comments?

Rowen: Small, cheap sensor nodes tend to be very analog-rich, lower scale in terms of digital content, and have lots of software. Part of understanding what’s different about standardization is built on understanding what’s different about the design process, and what does it mean to have a software-rich and analog-rich world.

Dai: Analog is important in this era of IoT. Analog needs to come into the standards community.

Richard Goering

Cadence Blog Posts About DAC 2015

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DAC 2015: Lip-Bu Tan, Cadence CEO, Sees Profound Changes in Semiconductors and EDA

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EDA investor and former executive Jim Hogan is optimistic about automotive electronics, but he has some concerns as well. At the recent Design Automation Conference (DAC 2015), he delivered a speech titled “The Looming Quality, Reliability, and Safety Crisis in Automotive Electronics...Why is it and what can we do to avoid it?"

Hogan gave the keynote speech for IP Talks!, a series of over 30 half-hour presentations located at the ChipEstimate.com booth. Presenters included ARM, Cadence, eSilicon, Kilopass, Sidense, SilabTech, Sonics, Synopsys, True Circuits, and TSMC. Held in an informal setting, the talks addressed the challenges faced by SoC design teams and showed how the latest developments in semiconductor IP can contribute to design success.

Jim Hogan delivers keynote speech at DAC 2015 IP Talks!

Hogan talked about several phases of automotive electronics. These include assisted driving to avoid collisions, controlled automation of isolated tasks such as parallel parking, and, finally, fully autonomous vehicles, which Hogan expects to see in 15 to 20 years. The top immediate priorities for automotive electronics designers, he said, will be government regulation, fuel economy, advanced safety, and infotainment.

More Code than a Boeing 777

According to Hogan, today’s automobiles use 50-100 microcontrollers per car, resulting in a worldwide automotive semiconductor market of around $40 billion. The global market for advanced automotive electronics is expected to reach $240 billion by 2020. Software is growing faster in the automotive market than it is in smartphones. Hogan quoted a Ford vice president who observed that there are more lines of code in a Ford Fusion car than a Boeing 777 airplane.

One unique challenge for automotive electronics designers is long-term reliability. This is because a typical U.S. car stays on the road for 15 years, Hogan said. Americans are holding onto new vehicles for a record 71.4 months.

Another challenge is regulatory compliance. Aeronautics is highly regulated from manufacturing to air traffic control, and the same will probably be true of automated cars. Hogan speculated that the Department of Transportation will be the regulatory authority for autonomous cars. Today, automotive electronics providers must comply with the ISO26262 automotive functional safety specification.

So where do we go from here? “We’ve got to change our mindset,” Hogan said. “We’ve got to focus on safety and reliability and demand a different kind of engineering discipline.” You can watch Hogan’s entire presentation by clicking on the video icon below, or clicking here. You can also watch other IP Talks! videos from DAC 2015 here.

https://youtu.be/qL4kAEu-PNw

Richard Goering

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Automotive Functional Safety Drives New Chapter in IC Verification

In 1985, as a relatively new editor at Computer Design magazine, I was asked to go forth and cover a new business called CAE (computer-aided engineering). I knew nothing about it, but I had been writing about design for test, so there seemed to be somewhat of a connection. Little did I know that “CAE” would turn into “EDA” and that I’d write about it for the next 30 years, for Computer Design, EE Times, Cadence, and a few others.

Now that I’m about to retire, I’m looking back over those 30 years. What a ride it has been! By the numbers I covered 31 Design Automation Conferences (DACs), hundreds of new products, dozens of acquisitions and startups, dozens of lawsuits, and some blind alleys that didn’t work out (like “silicon compilation”). Chip design went from gate arrays and PLDs with a few thousand gates to processors and SoCs with billions of transistors.

In 1985 there were three big CAE vendors – Daisy Systems, Mentor Graphics, and Valid Logic. All sold bundled packages that included workstations and CAE software; in fact, Daisy and Valid designed and manufactured their own workstations. In the early 1980s a workstation with schematic capture and gate-level logic simulation might have set you back $120,000. In 1985 OrCAD, now part of Cadence, came out with a $500 schematic capture package running on IBM PCs.

Cadence and Synopsys emerged in the late 1980s, and by the 1990s the EDA industry was pretty much a software-only business (apart from specialized machines like simulation accelerators). Since the early 1990s the “big three” EDA vendors have been Cadence, Synopsys, and Mentor, giving the industry stability but allowing for competition and innovation.

Here, in my view, are some of the highlights that occurred during the past 30 years of EDA.

EDA is a Highlight

The biggest highlight in EDA is the existence of a commercial EDA industry! Marching hand in hand with the fabless semiconductor revolution, commercial EDA made it possible for hundreds of companies to design semiconductors, as opposed to a small handful that could afford large internal CAD operations and fabs. With hundreds of semiconductor companies as opposed to a half-dozen, there’s a lot more creativity, and you get the level of sophistication and intelligence that you see in your smartphone, video camera, tablet, gaming console, and car today.

CAE + CAD = EDA. This is not just a terminology issue. By the mid-1980s it became clear that front-end design (CAE) and physical design (CAD) belonged together. The big CAE vendors got involved in IC and PCB CAD, and presented increasingly integrated solutions. People got tired of writing “CAE/CAD” and “EDA” was born.

The move from gate-level design to RTL. This move happened around 1990, and in my view this is EDA’s primary technology success story during the past 30 years. Moving up in abstraction made the design and verification of much larger chips possible. Going from gate-level schematics to a hardware description language (HDL) revolutionized logic design and verification. Which would you rather do – draw all the gates that form an adder, or write a few lines of code and let a synthesis tool find an adder in your chosen technology?

Two developments made this shift in design possible. One was the emergence of commercial RTL synthesis (or “logic synthesis”) tools from Synopsys and other companies, which happened around 1990. Another was the availability of Verilog, developed by Gateway Design Automation and purchased by Cadence in 1989, as a standard RTL HDL. Although most EDA vendors at the time were pushing VHDL, designers wanted Verilog and that’s what most still use (with SystemVerilog coming on strong in the verification space).

IC functional verification underwent huge changes in the late 1990s and early 2000s, largely due to new technology developed by Verisity, which was acquired by Cadence in 2005. Before Verisity, verification engineers were writing and running directed tests in an ad-hoc manner. Verisity introduced or improved technologies such as pseudo-random test generation, coverage metrics, reusable verification IP, and semi-automated verification planning. The Verisity “e” language became a widely used hardware verification language (HVL).

The biggest way that EDA has expanded its focus has been through semiconductor IP. Today Synopsys and Cadence are leading providers in this area. Thanks to the availability of design and verification IP, many SoC designs today reuse as much as 80% of previous content. This makes it much, much faster to design the remaining portion. While IP began with fairly simple elements, today commercially available IP can include whole subsystems along with the software that runs on them. With IP, EDA vendors are providing not only design tools but design content.

Finally, the EDA industry has done an amazing job of keeping up with SoC complexity and with advanced process nodes. Thanks to intense and early collaboration between foundries, IP, and EDA providers, tools and IP have been ready for process nodes going down to 10nm.

Where Does ESL Fit?

In some ways, electronic system level (ESL) design is both a lowlight and a highlight. It’s a lowlight because people have been talking about it for 30 years and the acceptance and adoption have come very slowly. ESL is a highlight because it’s finally starting to happen, and its impact on design and verification flows could be dramatic. Still, ESL is vaguely defined and can be used to describe almost anything that happens at a higher abstraction level than RTL.

High-level synthesis (HLS) is an ESL technology that is seeing increasing use in production environments. Current HLS tools are not restricted to datapaths, and they produce RTL code that gives better quality of results than hand-written RTL. Another ESL methodology that’s catching on is virtual prototyping, which lets software developers write software pre-silicon using SystemC models. Both HLS and virtual prototyping are made possible by the standardization of SystemC and transaction-level modeling (TLM). However, it’s still not easy to use the same SystemC code for HLS and virtual prototyping.

And Now, Some Lowlights

Every new industry has some twists and turns, and EDA is no exception. For example, the EDA industry in the 1980s and 1990s sparked a lot of lawsuits. At EE Times my colleagues and I wrote a number of articles about EDA legal disputes, mostly about intellectual property, trade secrets, or patent issues. Over the past decade, fortunately, there have been far fewer EDA lawsuits than we had before the turn of the century.

Another issue that was troublesome in the 1980s and 1990s was so-called “standards wars.” These would occur as EDA vendors picked one side or the other in a standards dispute. For example, power intent formats were a point of conflict in the early 2000s, but the Common Power Format (CPF) and the Unified Power Format (UPF) are on the road to convergence today with the IEEE 1801 effort. As mentioned previously, Verilog and VHDL were competing for adoption in the early 1990s. For the most part, Verilog won, showing that the designer community makes the final decision about which standards will be used.

How on earth did there get to be something like 30 DFM (design for manufacturability) companies 10-12 years ago? To my knowledge, none of these companies are around today. A few were acquired, but most simply faded away. A lot of investors lost money. Today, VCs and angel investors are funding very few EDA or IP startups. There are fewer EDA startups than there used to be, and that’s too bad, because that’s where a lot of the innovation comes from.

Here’s another current lowlight -- not enough bright engineering or computer science students are joining EDA companies. They’re going to Google, Apple, Facebook, and the like. EDA is perceived as a mature industry that is still technically very difficult. We need to bring some excitement back into EDA.

Where Is EDA Headed?

Now we come to what you might call “headlights” and look at what’s coming. My list includes:

• System Design Enablement. This term has been coined by Cadence to describe a focus on whole systems or end products including chips, packages, boards, embedded software, and mechanical components. There are far more systems companies than semiconductor companies, leaving a large untapped market that’s looking for solutions.
• New frontiers for EDA. At a 2015 Design Automation Conference speech, analyst Gary Smith suggested that EDA can move into markets such as embedded software, mechanical CAD, biomedical, optics, and more.
• Vertical markets. EDA has until now been “horizontal,” providing the same solution for all market segments. Going forward, markets like consumer, automotive, and industrial will have differing needs and will need optimized tools and IP.
• Internet of Things. This is a current buzzword, but the impact on EDA remains uncertain. Many IoT devices will be heavily analog, use mature process nodes, and be dirt cheap. Lip-Bu Tan, Cadence CEO, recently pointed out that the silicon percentage of IoT revenue will be small and that a lot of the profits will be on the service side.

Moving On

For the past six years I’ve been writing the Industry Insights blog at Cadence.com. All things change, and with this post comes a farewell – I am retiring in late June and will be pursuing a variety of interests other than EDA. I’ll be watching, though, to see what happens next in this small but vital industry. Thanks for reading!

Richard Goering

I have a set of pads for use in a design and I was wondering which attributes should I put on each pin.

Let's say it has the following pins:

   - inh_vdd, inh_vss, CORE, PAD where the first two are for the pad rings, the CORE pin is to use in the die and the PAD pin is the bonding pad.

I guess CORE would need:

   CLASS CORE

   USE POWER  (or GROUND if this happened to be a ground pad)

What about the inh_vdd and inh_vss? Theyu would not have the CLASS CORE, but would I use USE POWER/GROUND on them too?

   USE POWER (or GROUND)

   SHAPE ABUTMENT

And the bonding pad? Should I put it in the LEF? Or would that cause confusion to innovus or Voltus? And what attributed would it use? USE POWER/GROUND only?

Do I need anything in the LEF to indicate that the pin CORE and the pin PAD are essentially the same thing, just different places on the same power pad?

I have completed physical design of a block in innovus. I want to extract rc of that block using quantus .  It will be very helpful if you give step by step procedure and command to run quantus to extract rc of that block.

Hi,

I am doing layout for a mixed-signal circuit in Innovus. I want to create a digital donut style of layout (i.e. put analog circuit in the middle, and circle analog part with digital circuits).

To do that, I need to place some pins inside the edge to connect to analog circuit (as shown in my attachment), but the problems is that I cannot place pins inside the edge by using "pin editor" within Innovus. Any suggestions to place pins inside?

Thank you so much for your time and effort.

Hi All,

I have 1 huge project(day X) which has different reference power supply designs.

Now I start a new project and I require 1 specific reference power supply from X.

What is the easist way to do this, other than a copy paste.

Is there a way to create say symbols or something similar, so that multiple different people could use it if they need, in their projects

Thanks for your help and suggestions.

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Hi,

I am running simulation in ADEXL and need a custom function for VIVA to apply same procedure to all signals saved in output. For instance, I have clock nets and I want to get all of them and look at the duty-cycle, edge rate etc.

It is a little more involved than about part since I have some regex and setof to filter before processing but if I can get all signals for current history, I can postprocess them later.

In ocean, I am just doing outputs() and getting all saved signals but I was able to do this in VIVA calculator due to the difficulties in getting current history, test name and opening result directory

thanks

yayla

Version Info:

ICADV12.3 64b 500.21

spectre -W =>

Tool 'cadenceMMSIM' Current project version '16.10.479'
sub-version  16.1.0.479.isr9

I want to select a constraint, and then run a SKILL command that returns a list with the members of that constraint. Is this possible?

Thx,

I have a modeless informational dialog box defined at the beginning of a SKILL script, but its contents don't display until the script finishes.

How do you get a modeless dialog box contents to display while a SKILL script is running?

procedure(myproc()

   prog((myvars)

     hiDisplayAppDBox()    ; opens blank dialog box - no dboxText contents show until script completes!

     ....rest of SKILL code in script...launches child processes

   );prog

);proc

I have a top cell & need to revise all the instances' cellview & export top cell as a new GDS file.

So I write a SKILL code to do so and I find out it will be a little bit slow by using the dbSave to save the cellview of each instance.

Code as below:

let( (topCV subCV )
topCV = dbOpenCellViewByType(newLibName topCellName "layout" "maskLayout" "a")
foreach(inst topCV->instances
subCV = dbOpenCellViewByType(newLibName inst->cellName "layout" "maskLayout" "a")
;;;revise code content
;;;...
;;;revise code content
dbSave(subCV)
dbClose(subCV)
)
dbSave(topCV)
dbClose(topCV)
system(strcat( "strmout -library " newLibName " -topCell " topCellName " -view layout -strmFile " resultFolder "/" topCellName ".gds -techLib " srcLibName " -enableColoring -logFile " topCellName "_strmOut.log" ) )
)

Even if the cell content is not revised, the run time of dbSave will be 2 minutes when there are ~ 1000 instances in topcell. The exported GDS file size is ~2MB.

And the dbSave becomes the bottle neck of the code runtime...

Is there any better way to do such a thing? 

Hi there,

I'm running a transient simulation, and I want to get all instances with model implementation hisim_hv because after that I want to process the data and to adjust some parameters for this kind of devices before dumping the values.

What is the easiest/fastest way to get those instances in skill/ocean?

What I did until now: 

- save the final OP of the simulation and then in skill

openResults()
selectResults('tranOp)
report(?type "hisim_hv" ?param "vgs")

Output seems to be promising, and looks like I can redirect it to a file and after that I have to parse the file.

Is there other simple way? I mean to not save data to file and to parse it.

Eventually having an instance name, is it possible to get the model implementation (hsim_hv, bsim4, etc..)? 

Best Regards,

Marcel

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Hi all,

I have a question regarding the manufacture : how to check a cluster of same net vias spacing, with have no shape or cline covered

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Autonomous vehicles are coming. In a statistic from the U.S. Department of Transportation, about 37,000 people died in car accidents in the United States in 2018. Having safe, fully automatic vehicles could drastically reduce that number—but the trick is figuring out how to make an autonomous vehicle safe. Internet-enabled systems in cars are more common than ever, and it’s unlikely that the use of them will slow or stop—and while they provide many conveniences to a driver, they also represent another attack surface that a potential criminal could use to disable a vehicle while driving.

So—what’s being done to combat this? Green Hills Software is on the case, and they explained the landscape of security in automotive systems in a presentation given by Max Hinson in the Cadence Theater at DAC 2019. They have software embedded [FS1] in most parts of a car, and all the major OEMs use their tech. The challenge they’ve taken on is far from a simple one—between the sheer complexity of modern automotive computer systems, safety requirements like the ISO 26262 standard, and the cost to develop and deploy software, they’ve got their work cut out for them. It’s the complexity of the systems that represents the biggest challenge, though. The autonomous cars of the future have dynamic behaviors, cognitive networks, require security certification to at least ASIL-D, require cyber security like you’d have on an important regular computer system to cover for the internet-enabled systems—and all of this comes with a caveat: under current verification abilities, it’s not possible to test every test case for the autonomous system. You’d be looking at trillions of test cases to reach full coverage—not even the strongest emulation units can cover that today.

With regular cars, you could do testing with crash-test dummies, and ramming the car into walls at high speeds in a lab and studying the results. Today, though, that won’t cut it. Testing like that doesn’t see if a car has side-channel vulnerabilities in its infotainment system, or if it can tell the difference between a stop sign and a yield sign. While driving might seem simple enough to those of us that have been doing it for a long time, to a computer, the sheer number of variables is astounding. A regular person can easily filter what’s important and what’s not, but a machine learning system would have to learn all of that from scratch. Green Hills Software posits that it would take nine billion miles of driving for a machine learning system of today’s caliber to reach an average driver’s level—and for an autonomous car, “average” isn’t good enough. It has to be perfect.

A certifier for autonomous vehicles has a herculean task, then. And if that doesn’t sound hard enough, consider this: in modern machine-vision systems, something called the “single pixel hack” can be exploited to mess them up. Let’s say you have a stop sign, and a system designed to recognize that object as a stop sign. Randomly, you change one pixel of the image to a different color, and then check to see if the system still recognizes the stop sign. To a human, who knows that a stop sign is octagonal, red, and has “STOP” written in white block letters, a stop sign that’s half blue and maybe bent a bit out of shape is still, obviously, a stop sign—plus, we can use context clues to ascertain that sign at an intersection where there’s a white line on the pavement in front of our vehicle probably means we should stop. We can do this because we can process the factors that identify a stop sign “softly”—it’s okay if it’s not quite right; we know what it’s supposed to be. Having a computer do the same is much more difficult. What if the stop sign has graffiti on it? Will the system still recognize it as a stop sign? How big of an aberration needs to be present before the system no longer acknowledges the mostly-red, mostly-octagonal object that might at one point have had “stop” written on it as a stop sign? To us, a stop sign is a stop sign, even with one pixel changed—but change it in the right spot, and the computer might disagree.

The National Institute of Security and Technology tracks vulnerabilities along those lines in all sorts of systems; by their database, a major vulnerability is found in Linux every three days. And despite all our efforts to promote security, this isn’t a battle we’re winning right now—the number of vulnerabilities is increasing all the time.

Check back next time to see the other side: what does Green Hills Software propose we do about these problems? Read part 2 now.

If you missed the first part of this series, you can find it here.

So: what does Green Hills Software propose we do?

The issue of “solving security” is, at its core, impossible—security can never be 100% assured. What we can do is make it as difficult as possible for security holes to develop. This can be done in a couple ways; one is to make small code in small packs executed by a “safing plan”—having each individual component be easier to verify goes a long way toward ensuring the security of the system. Don’t have sensors connect directly to objects—instead have them output to the safing plan first, which can establish control and ensure that nothing can be used incorrectly or in unintended ways. Make sure individual software components are sufficiently isolated to minimize the chances of a side-channel attack being viable.

What all of these practices mean, however, is that a system needs to be architected with security in mind from the very beginning. Managers need to emphasize and reward secure development right from the planning stages, or the comprehensive approach required to ensure that a system is as secure as it can be won’t come together. When something in someone else’s software breaks, pay attention—mistakes are costly, but only one person has to make it before others can learn from it and ensure it doesn’t happen again. Experts are experts for a reason—when an independent expert tells you something in your design is not secure, don’t brush them off because the fix is expensive. This is what Green Hills Software does, and it’s how they ensure that their software is secure.

Now, where does Cadence fit into all of this? Cadence has a number of certified secure offerings a user can take advantage of when planning their new designs. The Tensilica portfolio of IP is a great way to ensure basic components of your design are foolproof. As always, the Cadence Verification Suite is great for security verification in both simulation and emulation, and JasperGold platform’s formal apps are a part of that suite as well.

We are entering a new age of autonomous technology, and with that new age we have to update our security measures to match. It’s not good enough to “patch up” security at the end—security needs to beat the forefront of a verification engineer or hardware designer’s mind at all stages of development. For a lot of applications, quite literally, lives are at stake. It’s uncharted territory out there, but with Green Hills Software and Cadence’s tools and secure IP, we can ensure the safety of tomorrow.