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[News24Wire] Edgar Rathbone , the Bulls' incoming chief executive, is undaunted by the prospect of having to manage Jake White , renowned for his hardnosed but highly successful methods.

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An Israeli security company said the hacking software, called Aria-body, had been deployed against governments and state-owned companies in Australia and Southeast Asia.

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The entire sports world was put on hold amid the coronavirus pandemic, leaving college seniors to ask, "What if?"

1 Russian Ruble = 0.05 United Arab Emirates Dirham

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1 Brunei Dollar = 2.5991 United Arab Emirates Dirham

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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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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Let’s face it – the EDA industry needs new people and new ideas. One of the best places to find both is academia, and a presentation at the Cadence Theater at the recent Design Automation Conference (DAC 2015) described collaboration models that are working today.

The presentation was titled “Industry/Academia Engagement Models – From PhD Contests to R&D Collaborations.” It included these speakers, shown from left to right in the photo below:

• Prof. Xin Li, Electrical and Computer Engineering, Carnegie-Mellon University (CMU)
• Chuck Alpert, Senior Software Architect, Cadence
• Prof. Laleh Behjat, Department of Electrical and Computer Engineering, University of Calgary

Alpert, who was filling in for Zhuo Li, Software Architect at Cadence, was the vice chair of DAC 2015 and will be the general chair of DAC 2016 in Austin, Texas. “My team at Cadence really likes to collaborate with universities,” he said. “We’re a big proponent of education because we really need the best and brightest students in our industry.”

Contests Boost EDA Research

One way that Cadence collaborates with academia is participation in contests. “It’s a great way to formulate problems to academia,” Alpert said. “We can have the universities work on these problems and get some strategic direction.”

For example, Cadence has been involved with the annual CAD contest at the International Conference on Computer-Aided Design (ICCAD) since the contest was launched in 2012. This is the largest worldwide EDA R&D contest, and it is sponsored by the IEEE Council on EDA (CEDA) and the Taiwan Ministry of Education. Its goals are to boost EDA research in advanced real-world problems and to foster industry-academia collaboration.

Contestants can participate in one of more problems in the three areas of system design, logic synthesis and verification, and physical design. The 2015 contest has attracted 112 teams from 12 regions. Cadence contributes one problem per year in the logic synthesis area. Zhuo Li was the 2012 co-chair and the 2013 chair. The awards will be given at ICCAD in November 2015.

Another step that Cadence has taken, Alpert said, is to “hire lots of interns.” His own team has four interns at the moment. One advantage to interning at Cadence, he said, is that students get to see real-world designs and understand how the tools work. “It helps you drive your research in a more practical and useful direction,” he said.

The Cadence Academic Network co-sponsors the ACM SIGDA PhD Forum at DAC, and Xin Li and Zhuo Li are on the organizing committee. This event is a poster session for PhD students to present and discuss their dissertation research with people in the EDA community. This year’s forum was “packed,” Alpert said, and it’s clear that the event needs a bigger room.

Finally, Alpert noted, Cadence researchers write and publish technical papers at DAC and other conferences, and Cadence people serve on the DAC technical program committee. “We try to be involved with the academic community on a regular basis,” Alpert said. “We want the best and the brightest people to go into EDA because there is still so much innovation that’s needed. It’s a really cool place to be.”

Research Collaboration Exposes Failure Rates

Xin Li presented an example of a successful research collaboration between CMU and Cadence. The challenge was to find a better way to estimate potential failure rates in memory. As noted in a previous blog post, PhD student Shupeng Sun met this challenge with a new statistical methodology that won a Best Poster award at the ACM SIGDA PhD Forum at DAC 2014.

The new methodology is called Scaled-Sigma Sampling (SSS). It calculates the failure rate and accounts for variability in the manufacturing process while only requiring a few hundred, or a few thousand, sample circuit blocks. Previously, millions of samples were required for an accurate validation of a new design, and each sample could take minutes or hours to simulate. It could take a few weeks or months to run one validation.

The SSS methodology requires greatly reduced simulation times. It makes it possible, Li noted, to run simulations overnight and see the results in the morning.

Li shared his secret for success in collaborations. “I want to emphasize that before the collaboration, you have to understand the goal. If you don’t have a clear goal, don’t collaborate. Once you define the goal, stick to it and make it happen.”

Contest Provides Learning Experience

Last year Laleh Behjat handed two of her new PhD students a challenge. “I told them there is an ISPD [International Symposium for Physical Design] contest on placement, and I expect you to participate and I expect you to win. Not knowing anything about placement, I don’t think they realized what I was asking them.”

The 2015 contest was called the Blockage-Aware Detailed Routing-Driven Placement Contest. Results were announced at the end of March at ISPD. And the University of Calgary team, despite its lack of placement experience, took second place.

Such contests provide a good learning tool, according to Behjat. Graduate students in EDA, she said, “have to be good programmers. They have to work in teams and be collaborative, be able to innovate, and solve the hardest problems I have seen in engineering and science. And they have to think outside the box.” A contest can bring out all these attributes, she said.

Further, Behjat noted, contest participants had access to benchmarks and to a placement tool. They didn’t have to write tools to find out if their results were good. Industry sponsors, meanwhile, got access to good students and new approaches for solving problems.

“You can see Cadence putting a big amount of time, effort and money to get students here and get them excited about doing contests,” she said. She advised students in the theater audience to “talk to people in the Cadence booth and see if you can have more ideas for collaboration.”

Richard Goering

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Hello, my name is Hsukang. I want to use Liberate to create a .lib file for the following circuit. This is a scan FF with two separate outputs.   The question is that no matter how I described its function, the synthesis tool said its a manformed scan FF.  Has anyone ever encountered anything like this？How should I describe the function correctly？I found that almost standard flip-flop cells are with only one output Q or have Qn at the same time. Does Liberate support scan flip-flop cells with two separate outputs ？

Thanks.