"Machine learning historically has come from a place where there was an emphasis on specific end applications, like object detection for automotive or …

The big data market is in a state of upheaval as companies begin shifting their data strategies from "nothing" or "everything" in the cloud to a strategic mix, squeezing out middle-market players and changing what gets shared, how that data is used, and how best to secure it. This has broad implications for the whole semiconductor supply chain, because in many cases it paves the way for more data to move freely between different vendors, no matter where they sit in that chain. That can go a long way toward improving the quality of chips and systems, reducing the cost of design and manufacturing, and shed light on supply chain constraints. It also opens up many more opportunities for data analysis to help offset rising concerns about liability in markets such as automotive, medical and mil/aero. "For years, the Fortune 500 to the Global 5,000 were reticent about moving to the cloud, but all of a sudden in the last 12 to 18 months there has been a massive shift to the cloud," said Michael Schuldenfrei, corporate technology fellow at Optimal Plus.

Data has been called the "new oil," and one reason for this is that personal data greases the wheels of our connected world. Data makes online advertising super-targeted (and super-profitable) for Internet giants like Google, and data about online habits is highly lucrative for any brand selling online (which is most of them). If data is the new oil, we're discovering gushers each and every day. Consultancy IDC predicts the total amount of data generated globally will hit 44 zettabytes by 2020, a tenfold jump from 2013's 4.4 zettabytes. The value of this new oil has been enhanced by artificial intelligence (AI) and machine learning systems that are able to make sense of it all.

Tools OneSpin revealed new formal applications focused on random fault verification for safety critical analysis in automotive and other mission-critical applications. The Fault Injection App provides controlled injection of faults and assertion mapping to associated fault scenarios, as well as visibility into corrupted design behavior. The Fault Detection App allows the detection of dangerous random faults or faults not detected by the safety mechanism. Additionally, the Fault Propagation App was updated to include a new debugger and support for SystemVerilog Assertions (SVA) and Property Specification Language (PSL). Cadence launched VirtualBridge Adapter, a virtual emulation technology allowing user applications and OS drivers to establish a virtual protocol connection to Palladium platforms.

Faster chips, more affordable storage, and open libraries are giving neural network new momentum, and companies are now in the process of figuring out how to optimize it across a variety of markets. The roots of neural networking stretch back to the late 1940s with Claude Shannon's Information Theory, but until several years ago this technology made relatively slow progress. The rush toward autonomous vehicles -- which relies on neural networking to collect data from many sensors -- changed all of that. Work is underway by established companies, startups, and universities around the globe, and funding is pouring into neural networking, as well as related markets such as embedded vision, machine learning, and artificial intelligence. "Mass market economics, increased processing power and improving computational vision techniques equals opportunities for new mass markets to be created," said Tim Ramsdale, general manager of the Imaging and Vision Group at ARM. "But all of this has to be done in real time. Having lights turn on as soon as you appear at the door is critical. That means a minimum of 30 frames per second, and preferably 60 frames per second. To do that you have to have processing at the edge, and processing at the edge means low power."

The answer isn't clear, because after decades of research and development, AI is finally starting to become a force to reckon with. The proof is in the M&A activity underway right now. Big companies are willing to pay huge sums to get out in front of this shift. The list goes on and on. AI has turned into an arms race among big companies, which are pouring billions of dollars into this field after a lull that lasted nearly a quarter of a century.

Depending upon your point of reference, artificial intelligence will be the next big thing or it will play a major role in all of the next big things. This explains the frenzy of activity in this sector over the past 18 months. Big companies are paying billions of dollars to acquire startup companies, and even more for R&D. In addition, governments around the globe are pouring additional billions into universities and research houses. A global race is underway to create the best architectures and systems to handle the huge volumes of data that need to be processed to make AI work. Market projections are rising accordingly.

Connected cars and the Internet of Things go together like peanut butter and jelly. But realizing the future of autonomous vehicles will demand close attention to be paid to cybersecurity, functional-safety standards, and other critical factors. IoT will advance the era of self-driving cars, which currently is dominated by Tesla Motors. At the same time, it will change some of the dynamics in this market. On one hand, it will turn automotive manufacturers into technology companies, which could provide new revenue streams for carmakers. On the other hand, it will open the door for new players that have never had a viable entry point in the automotive market. Consider the case of Velodyne LiDAR, a Morgan Hill, Calif.-based company, which last month opened a factory in nearby San Jose to manufacture its LIDAR product.

Semiconductor Engineering sat down to discuss the increasing reliance on architectural choices for improvements in power, performance and area, with Sundari Mitra, CEO of NetSpeed Systems; Charlie Janac, chairman and CEO of Arteris; Simon Davidmann CEO of Imperas; John Koeter, vice president of marketing for IP and prototyping at Synopsys; and Chris Rowen, a consultant at Cadence. What follows are excerpts of that conversation. SE: Can IP be designed for an entire system, and does that change what has to be done architecturally? Janac: If you are using layers and stacks, you can go all the way from layout into architecture for a particular piece of a chip. It gets used by the architect, by the RTL developer, by the layout person, by the verification engineer, for what is essentially a vertical slice of the chip.

Neural networking with advanced parallel processing is beginning to take root in a number of markets ranging from predicting earthquakes and hurricanes to parsing MRI image datasets in order to identify and classify tumors. As this approach gets implemented in more places, it is being customized and parsed in ways that many experts never envisioned. And it is driving new research into how else these kinds of compute architectures can be applied. Fjodor van Veen, deep learning researcher at The Asimov Institute in the Netherlands, has identified 27 distinct neural net architecture types. The differences are largely application-specific. Neural networking is based on the concept of threshold logic algorithms, which were first proposed in 1943 by Warren McCulloch, a neurophysiologist, and Walter Pitts, a logician.