Football stars Sergio Aguero and Arthur Melo have joined the grid for Formula One's esports event this Sunday.

Warriors president of basketball operations Bob Myers said his organization will be "good partners" if and when the NBA regular season resumes.

Haskell Women's Cross Country runner Tavia Hart comes out runner of the week after second meet of the season.

Men's and Women's Cross Country is ready to run today!

Differentiating good IP from mediocre or bad IP is getting more difficult, in part because it depends upon how and where it is used and in part, because even the best IP may work better in one system than another—even in chips developed by the same vendor.  

So, how do you measure IP quality and why it is so complicated?

The answer depends on who is asking. Most of the time, the definition of IP quality depends on your vantage point.  If you are an R&D manager, IP quality means something. If you are a global supply manager, IP quality means something else. If you are an SoC start-up, your measure of quality is quite different from that of an established fabless company. If you are designing IP in-house, then your considerations are very different than being a commercial IP vendor. If you are designing an automotive SoC, then we are in a totally different category. How about as an IP vendor? How do you articulate IP quality metrics to your customers?

This varies greatly by the type of IP, as well. When it comes to interface (hard) IP and controllers, if you are an R&D manager, your goal is to design IP that meets the IP specifications and PPA (power, performance, and area) targets. You need to validate your design via silicon test chips. This applies to all hard PHYs, which must be mapped to a particular foundry process. For controllers that are in RTL form—we called these soft IP—you have to synthesize them into a particular target library in a particular foundry process in order to realize them in a physical form suitable for SoC integration. Of course, your design will need to go through a series of design validation steps via simulation, design verification and passing the necessary DRC checks, etc. In addition, you want to see the test silicon in various process corners to ensure the IP is robust and will perform well under normal process variations in the production wafers.

For someone in IP procurement, the measure of quality will be based on the maturity of the IP. This involves the number of designs that have been taped out using this IP and the history of bug reports and subsequent fixes. You will be looking for quality of the documentation and the technical deliverables. You will also benchmark the supplier’s standard operating procedures for bug reporting and technical support, as well as meeting delivery performance in prior programs. This is in addition to the technical teams doing their technical diligence.

An in-house team that is likely to design IP for a particular SoC project will be using an established design flow and will have legacy knowledge of last generation’s IP. They may be required to design the IP with some reusability in mind for future programs. However, such reusability requirements will not need to be as stringent and as broad as those of commercial IP vendors because there are likely to be established metrics and procedures in place to follow as part of the design team’s standard operating procedures. Many times, new development based on a prior design that has been proven in use will be started, given this stable starting point. All of these criteria help the team achieve a quality outcome more easily.

Then, if designing for an automotive SoC, additional heavy lifting is required.  Aside from ensuring that the IP meets the specifications of the protocol standards and passes the compliance testing, you also must pay attention to meeting functional safety requirements. This means adherence to ISO 26262 requirements and subsequently achieving ASIL certification. Oftentimes, even for IP, you must perform some AEC-Q100-related tests that are relevant to IP, such as ESD, LU, and HTOL.

To read more, please visit: https://semiengineering.com/why-ip-quality-is-so-difficult-to-determine/

One reason for attending the upcoming Design Automation Conference (DAC 2015) is to learn about challenges other engineers have faced, and hear about their solutions. And the best place to do that is the Cadence Theater, located at the Cadence booth (#3515). The Theater will host continuous half-hour customer and partner presentations from 10:00 am Monday, June 8, to 5:30 pm Wednesday June 4.

As of this writing, 43 presentations are scheduled. This includes 17 customer presentations, 23 partner presentations, and 3 Cadence presentations, The presentations are open to all DAC attendees and no reservations are required.

Cadence customers who will be speaking include engineers from AMD, ams, Allegro Micro, Broadcom, IBM, Netspeed, NVidia, Renesas, Socionet, and STMicroelectronics. Partner presentations will be provided by ARM, Cliosoft, Dini Group, GLOBALFOUNDRIES, Methodics, Methods2Business, National Instruments, Samsung, TowerJazz, TSMC, and X-Fab.

These informal presentations are given in an interactive setting with an opportunity for questions and answers. Audio recordings with slides will be available at the Cadence web site after DAC. To access recordings of the 2014 DAC Theater presentations, click here.

This Cadence DAC Theater presentation drew a large audience at DAC 2015

Here’s a listing of the currently scheduled Cadence DAC Theater presentations. The latest schedule is available at the Cadence DAC 2015 site.

Monday, June 8

Tuesday, June 9

Wednesday, June 10

In a Wednesday session (June 10, 10:00 am) at the theater, the Cadence Academic Network will sponsor three talks on academic/industry collaboration models. Speakers are Dr. Zhou Li, architect, Cadence; Prof. Xin Li, Carnegie-Mellon University; and Prof. Laleh Behjat, University of Calgary.

As shown above, there will be a giveaways for a set of Bose noise-cancelling headphones, an iPad Mini, and a GoPro Hero3 video camera.

See the Cadence Theater schedule for further details. And be sure to view our Multimedia Site for live blogging and photos and videos from DAC. For a complete overview of Cadence activities at DAC, see our DAC microsite.

Richard Goering

Related Blog Posts

DAC 2015: See the Latest in Semiconductor IP at “IPTalks!”

Cadence DAC 2015 and Denali Party Update

DAC 2015: Tackling Tough Design Problems Head On

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

Here we conclude the blog series and highlight the results of Mediatek 's use of Cadence Perspec™ System Verifier for their SoC level verification. In case you missed it, Part 1 of the blog is here , and Part 2 of the blog is here . One of their key...(read more)

Cadence ® Spectre ® AMS Designer is a high-performance mixed-signal simulation system. The ability to use multiple engines, and drive from a variety of platforms enables you to "rev...

[[ Click on the title to access the full blog on the Cadence Community site. ]]

If I talk about my life, it was much simpler when I used to live with my parents. They took good care of whatever I wanted - in fact, they still do. But now, I am living alone, and sometimes I buy...

[[ Click on the title to access the full blog on the Cadence Community site. ]]

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.

So, what if you can figure out all that can go wrong when your product is being assembled early on? Not guess but know and correct at an early stage – not wait for the fabricator or manufacturer to send you a long report of what needs to change. That’s why Design for Assembly (DFA) rules(read more)

Hello, i need to test the  parameter Kf under some conditions in subthreshold.i cannot just plot the OP param,becasue i need to derive it under certain conditions.

Spectre(of Cadence) like BSIM(of Berkley) has developed a method for deriving each parameter in their model.

Is there a way to help me with such manual where i can test in cadence virtuoso the Kf parameter shown in the formula bellow?

Thanks.

I have made a customized menu in PCB Editor which I now would like to fill with content.

First of all I would like to have commands to add (or delete) layers in the board. I have parameter files (.prm) that describes both the stackup and the artwork for 2, 4, 6 and 8 layers.

I guess I could record a script (macro) where I use the "Import Parameter file" dialogue but this will get windows flickering by etc. Can I do this with SKILL instead?

I realize that it is possible (somehow) to do a SKILL-script that completely builds up the stackup and artworks for boards with different number of layers but I then have to edit the SKILL everytime I need to change anything. My thinking is that it perhaps is easier just to call the prm-file, which is easy to modify from within Allegro without knowing anything about SKILL.

I'm also looking for a solution to remove some Subclasses, containing certain keywords with a SKILL script but since I'm completely new to SKILL I don't really know where to begin.

Any assistance would be much appreciated.

I have a problem connected with my AXI4 coverage.

I enable coverage collection in AXI4 

      set_config_int("axi4_active_slave_agent_0.monitor.coverModel", "burst_started_enable", 1);
      set_config_int("axi4_active_slave_agent_0.monitor.coverModel", "coverageEnable", 1);

but i don't have a result.

I think the problem in Callback, but i try to connect all callback and i don't have positive result.

Can you help me?

Variants seem to be defined as present or not present.

Is there a variant that can assign different parts to the same reference designator? i.e.  R17 can be either 0 ohm 0805 jumper or 12k ohms 0805 resistor.

The simplest way I can think of is to use two parts with the same footprint and overlay them.

Is there a more functional way of doing this?  So that the variant would put the correct part in the BOM and the parts would of course have the same identical footprint.

Introduction to AMSD Flex mode and its benefits.(read more)

This blog talks about how to enable the AMS Designer flex mode.(read more)

Here is another blog in the multi-part series that aims at providing in-depth details of electromagnetic analysis in the Virtuoso RF solution. Read to learn about the nuances of port setup for electromagnetic analysis.(read more)

લૉકડાઉનને (Lockdown Part 3)4 મે થી આગામી બે સપ્તાહ સુધી વધારવાની જાહેરાત કરવામાં આવી છે. એટલે કે 17 મે સુધી લૉકડાઉન રહેશે

Flipkartએ લખ્યું કે, અમે ટૂંક સમયમાં પરત આવીશું, અને આપણે સાથે મળીને આ મુશ્કેલીથી બહાર આવીશું

લૉકડાઉન પાર્ટ-2 : કેન્દ્ર સરકારની નવી ગાઇડલાઇન્સ પ્રમાણે ભારતીય રિઝર્વ બેંક, બેંકો, એટીએમ તેમજ વીમા કંપનીઓ પહેલાની જેમ કામ કરતા રહેશે.

રિલાયન્સે ગત 31મી ડિસેમ્બરના રોજ Amazon અને Flipkartને ટક્કર આપવા માટે પોતાનું નવું ઈ-કૉમર્સ સાહસ 'જિયો માર્ટ' (JioMart) લૉંચ કર્યું હતું.

visit News18 Urdu for latest news, breaking news, news headlines and updates from Siddharth Nagar on politics, sports, entertainment, cricket, crime and more.

•Lockheed Martin deploys the 3DEXPERIENCE platform as an engineering and manufacturing planning toolset •Multi-year collaboration aims to speed timelines and improve efficiencies of next generation products •Digital experience platform approach drives advances in complex, sophisticated aircraft innovation

Dassault Systèmes Reports First Quarter Financial Results With Recurring Software, Operating Margin and EPS At the High End of Its Non-IFRS Guidance

BIOVIA Direct

This is an article discussing Apache2 Web Server hardening. Written in Turkish.