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Semiconductor Devices Transforming the World

Semiconductor Devices Transforming the World
by Daniel Nenni on 01-03-2018 at 7:00 am

As we begin another new year we begin another semiconductor conference cycle starting with SEMI ISS on January 15–18 at the Ritz-Carlton in Half Moon Bay California. This conference really sets the tone for the year and gives us a place to start thinking, acting, and reacting. This year it is all about the electronic devices we have all been working on and hearing about that will change the world, hopefully.

Smart, Intuitive & Connected: Semiconductor Devices Transforming the World
Something transformative is happening in electronics, taking many forms, shapes, and sizes. From stunning, 360-degree visions powered by augmented reality and intuitive behavior propelled by artificial intelligence, to perceptual computing within intelligent vehicles. Deep learning in robotics, the sleek functionality of smartphones, and the limitless connectivity within cloud — one variable resides at the core of so much innovation: the semiconductor silicon)

Through collaboration across an expanding ecosystem, our industry is delivering supremely sophisticated semiconductor devices, enabling the transformation of our world into a place where lifestyle and efficiency are optimized in ways never imagined. Indeed, through innovations in equipment, materials, design, and packaging, emerging application trends within electronics incorporate essential features that defy convention, including higher performance, less power consumption, smaller footprint, and heterogeneously integrated components.

To succeed in a transformational marketplace, shrewd business decisions are more critical than ever. Dynamic application markets, competitive product segments, and unprecedented industry consolidation make time-to-market a make-or-break proposition. ISS 2018 will explore strategy, discuss collaboration, examine threats, and expound upon the market opportunities empowered by today’s semiconductor technologies.

This year’s speakers come from a wide range of companies including our own Scott Jones of IC Knowledge. As you know Scott is the gold standard on process technology coverage here on SemiWiki.com. Scott is speaking on day 2 at 9:00 am on “The Impact of EUV on the Semiconductor Supply Chain”. Do not judge Scott by his picture on the SEMI site :rolleyes:. Scott is very approachable, a straight shooter, and will not dodge your questions, absolutely.

The other speaker companies include:

  • Accenture
  • Alpha Capital Partners
  • Amazon Web Services
  • ASE
  • ASML
  • BCA Research
  • Gartner
  • IBM
  • IC Knowledge
  • IHS Markit
  • Imec
  • Intel
  • Integrated Sensing Systems
  • McKinsey & Company
  • Mentor Graphics, a Siemens Business
  • Nissan Research Center Silicon Valley
  • Oculus
  • SEMI
  • Tufts University
  • Versum Materials

You can see the full agenda HERE.

The other must see presentation is “Predicting the Next Wave of Semiconductor Growth” by Dr. Walden Rhines President, and CEO of Mentor, a Seimens Business. Wally has his finger on the semiconductor pulse like no other and speaks from the heart and mind.

I will also be at the ISS CxO Panel “Nodes, Inter-nodes, and Real Nodesjust for the fun of it! Seriously, this should be one of the funniest panels ever:

A node is a node is a node could have once been considered a law of identity statement for semiconductor technology. Indeed, the term ‘node’ was invented to be a yardstick of accountability at its most basic level. On the one hand, it’s been distorted by marketing. On the other, Moore’s Law can’t keep up with the annual alarm clock set to the law that Christmas can’t be moved. This has led to internodes, as simple design revisions are no longer enough to have competitive products in the hotly contested holiday sales cycle. This panel brings together some of the world’s sharpest minds to untangle these issues and shed light on what the real nodes are.

Comparing IDM and foundry process nodes has been entertaining over the years but now that the foundries have caught up it is somewhat sad to see Intel trying to redefine leadership to their advantage, my opinion. I am interested to see what panelists John Chen, Ph.D. V.P. of Technology and Foundry Management, Nvidia and Antun Domic, Ph.D., Chief Technology Officer, Synopsys have to say. They are fabless experts and should have no problem cutting through the nonsense. Scott Jones has already covered this in detail: Intel Manufacturing Day: Nodes must die, but Moore’s Law lives! and 14nm 16nm 10nm and 7nm – What we know now which was widely read (more than 150,000 views) and commented on. Let’s see if this panel discussion is blog worthy…

I hope to see you there!

Also Read: 2017 in Review and 2018 Forecast!


Cryptocurrency is the New Target for Cybercriminals

Cryptocurrency is the New Target for Cybercriminals
by Matthew Rosenquist on 01-02-2018 at 12:00 pm

As predicted, the rise of cryptocurrency valuation has captured the attention of cybercriminals. New hacks, thefts, misuse, and fraud schemes are on the rise. Where there is value, there will be a proportional risk of theft.Criminals always pursue and exploit systems where they can achieve personal financial gain. It is the Willie Sutton effect: That’s where the money is.
Continue reading “Cryptocurrency is the New Target for Cybercriminals”


What’s old is new again – Analog Computing

What’s old is new again – Analog Computing
by Bernard Murphy on 01-02-2018 at 7:00 am

Once in a while I like to write on a fun, off-beat topic. My muse today is analog computing, a domain that some of us antiques in the industry recall with fondness, though sadly in my case without hands-on experience. Analog computers exploit the continuous nature of analog signals together with a variety of transforms to represent operations to solve real-value problems. In the early days, certain problems of this type were beyond the capabilities of digital computers, a notable example being finding solutions for differential equations. If you have taken a basic analog design course, you already know of an important transform relevant to this domain; an op-amp with a capacitative feedback loop acts as an integrator.


Coming out of the Second World War and moving onto the Cold War, Korea, Vietnam and other potential and real engagements, there was high interest in improving accuracy in firing control. This required solving, guess what, lots of differential equations defined by the mechanics of projectiles (thrust, gravity, air resistance, et al). Analog computing became hot in defense and aerospace and remained that way until digital (and later DSP) techniques caught up and surpassed these systems. Even the general public could get in on the action. Heathkit (another name from the past) sold a hobbyist system as early as 1960, long before most of us were thinking of digital computers.

But that was then. Are analog computers now just an obscure footnote in the history of computing? Apparently not. One hint was an article appearing recently in IEEE Spectrum. A team at Columbia University has been building integrated analog computers, where connectivity between analog components is controlled digitally. They are now on their third-generation chip.

These computers can solve problems (within their scope) within the order of a millisecond, though the solutions are accurate only to within a few percent, thanks to noise. The Columbia team view this as a good way to provide an approximate solution as input to a digital solver which can finish the job. Since finding an approximate solution is often the hardest part of solving / optimizing, the hybrid combination of analog and digital could be quite valuable. That said, there are plenty of challenges to overcome. One example is bounded connectivity in a 2-dimensional implementation. Functions can easily be constructed between neighboring components but connecting to other more distant functionality is generally fraught with problems for analog signals. Still, you could imagine that solutions might be found to this problem.

A more interesting (for me) possibility for analog/mixed-signal systems is around neuromorphic computing. What we are most familiar with in neural modeling is neural nets (NN) used for recognition applications, modeled using GPUs or DSPs or specialized hardware. But neural nets such as these are very simple models of how neurons really work. Real neurons are analog so any model has to mimic analog behavior at some level of accuracy (which is why DSPs are so good at this job). However, neuron behavior is more complex than the basic NN model (sum inputs, apply a threshold function, generate an output). For example, some inputs may reinforce or suppress other inputs (sharpening, which is related to remembering and forgetting).

More generally, inputs to a real neuron are not undifferentiated connections. Output(s) from a neuron to other neurons can be mediated by any one of multiple possible neurotransmitters with different functions, including the sharpening functions mentioned above. And all of this can be bathed in hormones secreted from various glands which further modulate the behavior of neurons. Who cares, you say? If one goal in building intelligent systems is to more closely mimic the behavior of the brain, then stopping at present day neural nets seems to be throwing in the towel rather too quickly, given real neuron complexity.

Which is why the Human Brain Project in Europe and the BRAIN Initiative in the US are working to jointly advance neuroscience and related computing. This has driven quite a bit of development in neuromorphic compute systems, such as the Neurogrid developed at Stanford. What is especially interesting about many of these systems is the significant use they make of analog computation together with digital methods. Here, differential equations play no part (as far as I know). The motivation seems much more around low-power operation (Stanford cite a 10[SUP]5[/SUP] reduction in power over an equivalent supercomputer implementation) and a tolerance to analog noise-related inaccuracies in this application. After all, real neurons aren’t hyper-accurate and NN implementations for inferencing are already talking about 1- or 2-bit accuracy being sufficient for image recognition.

The constraints faced by the Columbia work don’t play such a big role here. In using analog to model neuron behaviors, 2D bounds on a chip reflect physical bounds in the brain (and if you need to go 3D, presumably that would be possible too with stacking). So maybe the big comeback for analog computing will be as a close partner with digital in neuromorphic computing. Perhaps someday this approach may even replace neural nets?


IBM Plays With The AI Giants With New, Scalable And Distributed Deep Learning Software

IBM Plays With The AI Giants With New, Scalable And Distributed Deep Learning Software
by Patrick Moorhead on 01-01-2018 at 11:00 am

I’ve been following IBM’s AI efforts with interest for a quite a while now. In my opinion, the company jump-started the current cycle of AI with the introduction of Watson back in the 2000s and has steadily been ramping up its efforts since then. Most recently, I wrote about the launch of PowerAI, IBM’s software toolkit solution to use with OpenPOWER systems for enterprises who don’t want to develop their AI solutions entirely from scratch but still want to be able to customize to fit their specific deep learning needs. Today, IBM Research announced a new breakthrough that will only serve to further enhance PowerAI and its other AI offerings—a groundbreaking Distributed Deep Learning (DDL) software, which is one of the biggest announcements I’ve tracked in this space for the past six months.

Getting rid of the single-node bottleneck

Anyone who has been paying attention knows that deep learning has really taken off in the last several years. It’s powering hundreds of applications, in consumer as well as business realms, and continues to grow. One of the biggest problems holding back the further proliferation of deep learning, however, is the issue of scalability. Most AI servers today are just one single system, not multiple systems combined. The most popular open-source deep learning software frameworks simply don’t perform well across multiple servers, creating a time-consuming bottleneck. In other words, while many data scientists have access to servers with four to eight GPUs, they can’t take advantage of it and scale beyond the single node—at the end of the day, the software just wasn’t designed for it.

Enter the IBM DDL library: a library built with IBM Research’s unique clustering methods, that links into leading open-source AI frameworks (such as TensorFlow, Caffee, Torch, and Chainer). With DDL, these frameworks can be scaled to tens of IBM servers, taking advantage of hundreds of GPUs—a night and day difference from the old model of doing things. To paint a picture, when IBM initially tried to train a model with the ImageNet-22K data set, using a ResNet-101 model, it took 16 days on a single Power “Minsky” server, using four NVIDIA P100 GPU accelerators. A 16-day training run means a significant delay of time to insight, and can seriously hinder productivity.

IBM is calling DDL “the jet engine of deep learning”—a catchy moniker that honestly isn’t too far off the mark in my opinion. Using DDL techniques, IBM says it was able to cut down that same process to a mere 7 hours, on 64 Power “Minsky” servers, with a total of 256 NVIDIA P100 GPU accelerators. Let me reiterate that: 16 days, down to 7 hours. If these results are accurate, which I think they are, it’s clear why IBM thinks it has a real game-changer on its hands. IBM’s new image recognition record of 33.8% accuracy in 7 hours handily surpasses the previous industry record set by Microsoft—29.9% accuracy in 10 days. To top it all off, IBM says DDL scales efficiently—across up to 256 GPUs, with up to 95% efficiency on the Caffe deep learning framework.

Now available in beta

Developers won’t have to wait to try out this new technology. IBM research is delivering a beta version of the DDL to IBM Systems, which is available now in the newly announced 4th revision of IBM’s PowerAI (for TensorFlow and Caffe, with Torch and Chainer to follow soon). I think this will be a great addition to IBM’s Power systems, which I’ve called the “Swiss Army knives of acceleration”—standard PCI express, CAPI, and NVLink, all wrapped up in one platform.

Another unique thing of note about DDL is that it will be available not only on-prem but also through the cloud—via a cloud provider called Nimbix. In today’s hybrid environment, this flexibility is obviously a plus. Developers can try it out beta version now on Nimbix, or on an IBM Power Systems server.
Wrapping up

One of the most interesting things for me is that this new technology is coming from IBM, not one of the flashier, louder AI proponents like Google or Facebook. It looks like if IBM can continue to bring “firsts” to the table, IBM is really shaping up to be not just a major player in the enterprise, but for deep learning overall. DDL and OpenPOWER are the secret sauce that I think will give IBM an edge it needs—significantly cutting down training times, and improving accuracy and efficiency. I’ll continue to watch with interest, but I think by getting rid of this bottleneck, DDL has the potential to really open the deep learning floodgates. It could be a real game-changer for IBM, PowerAI, and OpenPOWER.


HDMI 2.1 Delivers 48.0 Gbps & Supports Dynamic HDR

HDMI 2.1 Delivers 48.0 Gbps & Supports Dynamic HDR
by Eric Esteve on 01-01-2018 at 7:00 am

You may or may not have bought HDMI-equipped device for black Friday or during year end break, but you TV set (or/and you PC) are certainly HDMI-powered, like the 750 million HDMI-equipped devices sold in 2016. In fact, cumulated shipment of HDMI-equipped devices has reached 6 BILLION since the protocol introduction in 2003! HDMI 1.0 was delivering 4.5 Gbps, enough to support 1080p standard, and HDMI 2.1 delivers more than 10x with 48 Gbps. We have to remember that HDMI protocol is unidirectional, unlike USB or PCI Express, and the function is built by using four PHY, each delivering 12 Gbps.


What about HDMI competition? We can forget about Diiva, born in the early 2010’s (and disappearing just a couple of years later). DisplayPort, launched by VESA in 2006 could be seen as a direct competitor at that time, but HDMI was supported by much stronger marketing from Silicon Image and HDMI Licensing LLC (founded by Hitachi, Panasonic, Philips, Silicon Image, Sony, Thomson (RCA) and Toshiba). DisplayPort protocol is now mostly used to connect a computer monitor to a PC, but is not active in the consumer TV segment.

You may have heard about ThunderBolt (and if you use it you are more likely an Apple customer!). The protocol is not point to point like HDMI, but daisy chained: a single Thunderbolt port can support up to six Thunderbolt devices. Looks smart, but ThunderBolt penetration was penalized by higher price, as only high-end devices were equipped, and also by the lack of available IP as Intel didn’t want to license the technology as a design IP… This was not the case with HDMI and we can see that HDMI Licensing strategy, more open compared with ThunderBolt, has allowed this huge market penetration: HDMI is now ubiquitous in the consumer/computer segments when TV is concerned.



As we mention IP, it’s interesting to notice the market evolution for HDMI protocol. If Silicon Image has been the undisputed leader for years since HDMI introduction, for ASSP as well as for IP sales, their IP sales went down dramatically, the IP group was sold to Lattice who eventually sold it to Invecas. And, like for most of the protocol based interface IP, Synopsys is now the clear leader, as you can see on the above figure from IPnest that Synopsys includes in their HDMI pitch (which make me proud…).

In fact, the strongest Synopsys competitor is made of internal design teams developing their own HDMI IP. It was probably not so difficult to design 1.5 Gbps SerDes for HDMI 1.0, but the last protocol release, HDMI 2.1, has to deliver 48 Gbps over 4 PHY. The solution requires using 12 Gbps SerDes to deliver 48 Gbps aggregate bandwidth for uncompressed 8K resolution at 60 Hz refresh rate. But the speed is only one part of the equation as the HDMI 2.1 solution from Synopsys also supports new Dynamic HDR, eARC, VESA DSC 1.2a and HDCP 2.2.



If we compare HDMI 2.1 with DisplayPort 1.4, both are supporting 8K video, but the difference is that HDMI allow supporting uncompressed 8K. High Dynamic Range (HDR) feature is also known as Dolby Vision and has been implemented in HDMI 2.0a (released on April 8, 2015) and DisplayPort 1.4 (released on March 1, 2016). With HDMI 2.1 standard, released on January 2017, Hybrid Log-Gamma (HLG) support had been added to the HDMI 2.0b standard, allowing now to claim Dynamic HDR support. Dynamic HDR is dynamic metadata that allows for changes on a scene-by-scene or frame-by-frame basis.

Because most of the TVs are used with soundbars, it was important to make life easier for customers, and that’s one of the goals of eARC, as it simplifies connectivity and discovery between TVs and soundbars. eARC also supports most advance audio formats and highest audio quality.

To provide smoother, lag-free and more fluid gaming experience, Synopsys has implemented Enhanced Refresh Rates: Variable Refresh Rates (VRR), Quick Media Switching (QMS), Quick frame Transport (QFT) or Auto Low Latency Mode (ALLM).

No doubt that your next TV set will be HDMI 2.1-equipped!

By Eric Esteve from IPnest


2017 in Review and 2018 Forecast

2017 in Review and 2018 Forecast
by Daniel Nenni on 12-30-2017 at 7:00 am

This has been an amazing year for me both personally and professionally. Personally we are now empty nest and have our first grandchild. SemiWiki is prospering, a company that I have been involved with for ten years (Solido Design) had a very nice exit, and my time promoting semiconductor stocks to Wall Street paid off with the PHLX Semiconductor index (SOX) gaining an astounding 40%.
Continue reading “2017 in Review and 2018 Forecast”


Why Bitcoin is the largest Ponzi scheme in human history

Why Bitcoin is the largest Ponzi scheme in human history
by Vivek Wadhwa on 12-29-2017 at 12:00 pm

During the late ’90s, Silicon Valley venture capitalists and New York City investment bankers used phrases such as “monetizing eyeballs,” “stickiness,” and “B2C” to justify the ridiculous valuations of Internet companies. They claimed conventional methods were inapplicable in valuing the dot-com companies — which had no revenue — because we were entering an entirely new economy.

Believing these people, and afraid to miss out on the gold rush, small-time investors, grandma and grandpa, and barbers and taxi drivers invested their life savings in companies such as Pets.com, Webvan, and eToys. The bubble burst, and they lost everything. Through a transfer of wealth in the billions of dollars from Main Street to Wall Street, VCs, unscrupulous CEOs, and bankers had effectively enriched themselves at the expense of hundreds of thousands of ordinary investors, leaving them to despair about their futures.

History is repeating itself now with Bitcoin. This time, it isn’t just Main Street U.S.A. that is about to lose its shirt; it is also the developing world. Technology has made it possible for hypesters in Silicon Valley, China, and New York City to fleece anyone, anywhere, who has a bank account and an Internet connection.

The story that Bitcoin victims are being sold is that, because we cannot trust government-issued currencies, Bitcoin is the future of money. One investor calls Bitcoin “a gift from God to help humanity sort out the mess it has made with its money.” A PayPal director predicts that Bitcoin’s price will reach $1 million in the next five to 10 years; asset managers say it is the new gold.

This is complete nonsense. Yes, the price of Bitcoin may yet double or even quadruple — because its price is based on pure speculation, and these stories are feeding such speculation. But Bitcoin’s market price is almost certain at some point to crash and burn, just as the dot-coms did, and for the same reason: because it is all hype. And there will be no one to turn to when it does, because no government or bank is backing it up; and the people who are hyping Bitcoin will have cashed out and be long gone.

Bitcoin’s price is not a reflection of its growing usage as currency; it reflects merely demand for the mirage of its speculative value. Its price is rising only because people all over the world are hearing stories of how others doubled or tripled their money in a short period — and they don’t want to miss out. Unsophisticated investors are taking out loans to buy Bitcoins. Those who have spent the currency feel remorseful when they see its price subsequently increase, so they hoard it.

Bitcoin was invented by an unknown person or group to be a digital currency. It allows money to be transferred directly between individuals using cryptography. The bank ledger is distributed to all users, and complex mathematical transactions ensure transaction integrity. Such a system makes it difficult for governments to know the identities of people exchanging money, so it has become a haven for money laundering, drug dealing, and corruption.

Beyond its usability for crime, Bitcoin has major design flaws.

Bitcoins are created (or “mined”) at predetermined and gradually decreasing rates, with a total limit of 21 million issuable coins. The rate of increase in available Bitcoins is not keeping pace with the number of people keen to buy them, so the price of a Bitcoin keeps increasing. Because its price increases, both its “miners,” whose computers do complex calculations to earn the currency, and those who buy Bitcoins from others feel reluctant to use them as currency by spending them. Instead, they sit on their coins while they wait for the price to rise further. With Bitcoin supply constrained and increasingly falling short of demand, instead of functioning as a currency, Bitcoin is a speculative empty asset.

Then, there are problems with the technology itself.

First, anyone who has access to a Bitcoin password (or private key) has the authority to spend the Bitcoins it unlocks; loss of the password means loss of all of the associated Bitcoins, with no recourse. Second, linear growth in the chain of blocks that make up Bitcoin is resulting in exponential growth in the computation necessary to process and verify transactions: transactions that used to take 10 minutes now take hours. Third, with Bitcoin transactions fees hovering above $25, a $5 payment now costs $30. This obviously is not a workable digital currency.

What is most worrisome for the planet is the energy expenditure that verifying transactions now requires. The Bitcoin network is reportedly consuming energy at an annual rate of 32TWh — about as much as the entire nation of Denmark. Each transaction consumes 250kWh, enough energy to power an average Western home for nine days. China has become the dominant Bitcoin-mining nation, with its provinces providing ultra-cheap energy to miners.

Digital currencies surely are the future, but other options make more sense than Bitcoin. Take China’s WeChat Pay and Alipay, which now process $5.5 trillion of payments. Or India’s Unified Payments Interface, which makes it possible to transfer money between people within seconds — for no fee. This occurs bank to bank, provides customer support and security, and has little overhead. So there are better and simpler ways.

For more, follow on Twitter: @wadhwa and visit my website: www.wadhwa.com


Autonomous Vehicles Upending Automotive Design Process

Autonomous Vehicles Upending Automotive Design Process
by Tom Simon on 12-28-2017 at 12:00 pm

The automotive industry has a history of bringing about disruptive technological advances. One only needs to look at the invention of the assembly line by Henry Ford to understand the origins of this phenomenon. Today we stand on the brink of a massive change in how cars operate and consequently how they are built. A number of automotive manufactures have promised autonomous vehicles by 2021. The move toward this objective will be a continuous progression of adding technology at each step of the way over the next 3 or 4 years. At the same time consumer expectations about usability and reliability will be huge market drivers.

The design challenges associated with these changes will be enormous. Cars are already probably the most complex consumer items made today. With the addition of the sensor, communications, processing and power train enhancements that are expected, overall complexity will increase. Numerous architectural and design decisions will have to be made. Puneet Sinha, Automotive Manager at Mentor and John Blyler, System Engineer/Program Manager at JB Systems have completed a white paper entitles “Key Engineering Challenges for Commercially Viable Autonomous Vehicles” that delves into the specifics of what we can expect to see coming down the road.

They break the topics involved into 6 different categories. Starting with sensors, they explain how the number of sensors in an automobile is going to grow from the current number between 60 and 100 up to a much higher number. To fully implement 360 degree ‘vision’ a variety of different types of sensors are needed. The diagram below provides an overview of which types of sensors are used for specific tasks. The operating environment for each of these sensors is challenging. The goal of reducing sensor size conflicts directly with thermal management requirements. Automobiles also have to operate in extremely cold and hot environments and often in conjunction with other automotive systems, which exacerbates thermal and reliability issues.

The section I found most interesting discussed sensor fusion. From the advent of the internet of things there has been a push to move sensor fusion to the edge. It made a lot of sense to use smaller processors adjacent to, or integrated with, the sensor to take raw sensor data and convert it into more easily digestible data, which also has the benefit of being smaller in size. However, the Mentor paper points out that in future automobiles there will literally be hundreds of these smaller MCU’s distributed throughout the vehicle. This can lead to reliability issues, as well as potentially creating thermal and power issues. They also point out that edge sensor fusion can lead to an exploding BOM.

Mentor’s approach is to use the high speed data busses on the vehicle to transport raw data and centralize sensor fusion. Mentor has announced a product called DRS360 that enables this centralized fusion approach. They also point out that the raw data can be combined in unique ways if it is processed centrally, thus enabling higher quality results. The end result is a 360 degree perspective of the car and its environment. Their experience implementing this has shown that the overall power usage is dramatically reduced.

The third area they discuss is the electronic and electrical architecture. The automotive wiring harness is actually the third heaviest component in a vehicle. It has grown from the original point to point wire of the first cars to multiple communications networks, each with a specific purpose – Controller Area Network (CAN), Local Interconnect Network (LIN) and Automotive Ethernet. Because there is so much interplay between the electronics connectivity and the physical design of the vehicle, significant planning and interdisciplinary design is required. In many ways solving this design problem looks a lot like place and route used on SOC’s.

The power train is another design domain that is undergoing rapid change due to the advent of electric vehicles. This can include both hybrid and fully electric drive systems. It turns out that autonomous vehicles will have different design parameters than human piloted vehicles. One surprising piece of information from the Mentor white paper is that autonomous drive vehicles will not need to be designed for the so called 90th percentile driver, who can be very rough on the drive train. There are other implications in this area from full autonomous driving.

There will also be dramatic changes in vehicle safety and in-cabin experience. The interior of a fully autonomous vehicle will be quite different from today’s vehicles. Occupants will interact even more with navigation and entertainment systems. Passenger displays will also change quite a bit. Safety requirements will pervasively affect every element of the automotive design process.

Last but not least the white paper addresses vehicle connectivity. The moniker applied to this area is V2V communications. Cars will be communicating with the cloud, each other and possibly the road and other infrastructure elements. There is great opportunity to increase safety and situational awareness in the automotive navigational and safety systems.

Mentor has a comprehensive suite of tools that address these design areas and challenges. The white paper does an excellent job of detailing each of the design areas and also lays out the relevant tools that can be used to deal with the system level integration problems in those areas. I recommend a thorough read of the white paper to fully understand the design challenges and to learn which Mentor tools can address them. Mentor has a long history of working at the system level, and the coming changes in the automotive space are creating a lot of opportunity for them to become a major player.


A Picture is worth a 1,000 words

A Picture is worth a 1,000 words
by Daniel Payne on 12-28-2017 at 7:00 am

Semiconductor IP re-use is a huge part of the productivity gains in SoC designs, so instead of starting from a clean slate most chip engineers are re-using cells, blocks, modules and even sub-systems from previous designs in order to meet their schedule and stay competitive in the market place. But what happens when you intend to re-use some IP with the notion of adding some new features to it? How in the world do you learn about a previous IP block if you weren’t the person responsible for creating it in the first place? If someone hands you 10,000 lines of VHDL or SystemVerilog code, how would you go about learning how it was created in order to modify it? Sure, you could read the documentation, or even start to just look at the source code to glean some insight. Is there a better way? Yes, there is a better way and that better way is to read in your HDL code and then automatically create a graphical representation of it using blocks and state machines.

Sigasi is an EDA company with a tool that does just that, however at first use of their BlockDiagram view you may just see a bunch of cell instances connected with wires which can be a bit messy to infer much info from:

If you could color instances and use some busses then the block diagram would be more legible and make understanding its operation a whole lot easier:

The Sigasi Studio tool also automatically finds state machines in your HDL code and creates a StateMachine view, which is how most IC designers think of their logic to start out with:

Here’s an updated view of the same state machine, this time with some coloring and re-grouping to emphasize state transitions:

So how do you go from the default diagrams created by Sigasi Studio to the ones with colors and groupings?

You use something called a Graphic Configuration file. Some of the benefits of using a text file for Graphic Configuration are:

  • Easier to manager with your favorite version control system, allowing easy compares and merges.
  • Debug is straight forward, saving you time.
  • Sigasi Studio features like auto-complete, validations and formatting are all built-in to the tool.

With a Graphic Configuration file you can do five important tasks:
[LIST=1]

  • Group states, instances or wires together.
  • Hide states or blocks.
  • Collapse blocks.
  • Coloring, as shown in the two examples above
  • Regex matching.

    Some Examples
    Let’s say that you want to color a specific block to green and have the internals hidden, the Graphic Configuration file syntax is:block my_block { color green collapse }


    That was pretty simple and compact to write.

    Continuing that first example a bit, now we want to access a block within a block, calling for a nest configuration like this:
    block my_block {

    [INDENT=2]color green
    block block_within_name {

    [INDENT=3]color red

    [INDENT=2]}

    }

    Changing how your state machine looks is just like the block diagram we just learned except the first keyword is “state” instead of “block”.

    Summary
    A picture really is worth a 1,000 words, and now with the automated visualization feature in Sigasi Studio you can have a lot more control over how it looks by using a Graphic Configuration file, therefore making understanding your HDL code much easier than staring at text alone. To read more about this topic there’s a blog article written by Titouan Vervack at the Sigasi site.


  • Lipstick on the Digital Pig

    Lipstick on the Digital Pig
    by Bill Montgomery on 12-27-2017 at 12:00 pm

    I have a lot of friends in the real estate industry, and two of the most common sales tactics are to create “curb appeal,” and to “stage” the interior of the residence being sold. Curb appeal, of course, refers to making the home looks as appealing as possible upon first impression. Update the landscaping. Add flowers. Make sure the lawn is well maintained. Maybe add a coat of fresh paint. You get the idea. And on the inside, remove everything that made the house a home, and bring in a professional interior designer to “stage” the place by painting, enhancing lighting, bringing in rental furniture etc. – essentially, transitioning the abode to a “model home” that is highly pleasing to the eye.

    If the house is in need of better wiring, updated plumbing, new home heating/cooling – Hell, if the foundation is crumbling, it doesn’t matter. The goal is to make the prospective buyer feel good about the property and envision living in this “beautiful” residence.

    Putting “lipstick on the real estate pig” works. Professionals will tell you that creating curb appeal and a well-staged home will sell faster, and for more money than a comparable house whose agent/seller does not adopt these tactics.

    We have a similar situation occurring in the world of cybersecurity, particularly in the emerging world of IoT. We have a “Digital Pig” that is part of our everyday connected existence, and layers of brightly colored lipstick are being slapped on this porker every single day. I’m referring to PKI – a system for the creation, storage and distribution of digital certificates which are used to verify that a particular public key belongs to a certain entity. To be clear, it’s the elaborate PKI needed to support the certificates that’s the problem. And the sad reality is if certificate issuance and key exchange is involved, the cyber security solution is doomed from the start.

    We’re talking PIG KI.

    Why is that?

    It’s because the entire certificate industry has been so badly compromised by fake, flawed, highly-vulnerable self-signed and un-revoked certificates, that it is beyond repair. It’s not like issuing new certificates and adding greater capabilities in certificate management or better cyber intrusion detection can eliminate the problem. The Digital Pig is insidethe connected-world barn, and closing the doors after it’s already pervasively entrenched in our cyber spaces just won’t work. And this isn’t just my opinion. According to a Sept 2016 article posted by the EU Agency for Network and Information Security (ENISA), “Certificate Authorities are the weak link of Internet Security.”

    Symantec knows it. It finally gave up on the certificate issuance game, selling its website security business to DigiTrust after Google and Mozilla began a process of distrust in its TLS certificates.

    But how bad can things be, really? The situation is well beyond bad – it’s horrifying. According to Netcraft a whopping 95% of HTTPS servers are vulnerable to Man-in-the-Middle attacks. How is that possible? Well, for sure human error and technical incompetence is part of the problem. But that’s never going away. The real problem is that the reliance on certificates. Flawed, broken, faked certificates.

    Certificates: Impossible to Kill
    Of the certificates already in use, the Ponemon Institute reports that 54% of organizations do not know how many certificates are in use within their infrastructures, where they are located or how they are used – and they have no idea how many of these unknown assets are self-signed (open source) or signed by a Certificate Authority.

    Netcraft’s Mutton writes, “ killing bad certs is difficult…it is not unusual to see browser vendors making whole new releases in order to ensure that the compromised – or fraudulent – certs are no longer trusted…it could remain trusted…for months or years.”

    I’ll argue that certificates, being the root of the problem, have to be eliminated. The recent discovery of the ROCA and ROBOT attack highlights the serious vulnerability of the extant implementations of the RSA algorithm and that RSA itself ought to be deprecated.

    What’s the solution? Kill the PIG! Start with the premise that cybercriminals can’t get in the middle of communication protocols that don’t exist.
    #Certificate-less