Seeing The Future Of Vision

Vision systems have evolved from cameras that enable robots to “see” on a factory floor to a safety-critical element of the heterogeneous systems guiding autonomous vehicles, as well as other applications that call for parallel processing technology to quickly recognize objects, people, and the surrounding environment. Automotive electronics and mobile devices currently dominate embedded... » read more

The Looming AI War

A recent spate of acquisitions and announcements in AI and machine learning is setting the stage for a colossal showdown across the tech industry. Among those vying for top spots are Samsung, Google, Apple, Microsoft and Amazon, each with a large enough revenue stream to support an M&A feeding frenzy and the sustained investments required to remain competitive. Consider the most recent a... » read more

Alexa, Can You Help Me Build A Better SoC?

Consumers have fallen love with clever products like Amazon Echo, Nest, Google maps, Waze and Zillow that somehow make life a little easier and more fun. The underlying technology that makes these apps so rich and useful is machine learning and it seems to be showing up everywhere. Maybe it’s time to ask, “Alexa, can you help me build a better SoC?” The Next Frontier in SoC Architectur... » read more

Building Chips That Can Learn

The idea that devices can learn optimal behavior rather than relying on more generalized hardware and software is driving a resurgence in artificial intelligence, machine leaning, and cognitive computing. But architecting, building and testing these kinds of systems will require broad changes that ultimately could impact the entire semiconductor ecosystem. Many of these changes are wel... » read more

Grappling With Manufacturing Data

As complexity goes up with each new process node, so does the amount of data that is generated, from initial GDSII to photomasks, manufacturing, yield and post-silicon validation. But what happens to that data, and what gets shared, remain a point of contention among companies across the semiconductor ecosystem. The problem is that to speed up the entire design through manufacturing process,... » read more

Executive Insight: Sundari Mitra

Sundari Mitra, co-founder and CEO of [getentity id="22535" e_name="NetSpeed Systems"], sat down with Semiconductor Engineering to discuss machine learning, shifting from a processor-centric to a memory-centric design, and what needs to change to make that all happen. What follows are excerpts of that conversation. SE: What is the biggest change you’re seeing? Mitra: We go through a cycl... » read more

Plugging Holes In Machine Learning

The number of companies using machine learning is accelerating, but so far there are no tools to validate, verify and debug these systems. That presents a problem for the chipmakers and systems companies that increasingly rely on machine learning to optimize their technology because, at least for now, it creates the potential for errors that are extremely difficult to trace and fix. At the s... » read more

What’s Missing From Machine Learning

Machine learning is everywhere. It's being used to optimize complex chips, balance power and performance inside of data centers, program robots, and to keep expensive electronics updated and operating. What's less obvious, though, is there are no commercially available tools to validate, verify and debug these systems once machines evolve beyond the final specification. The expectation is th... » read more

New Architectures, Approaches To Speed Up Chips

The need for speed is back. An explosion in the amount of data that needs to be collected and processed is driving a new wave of change in hardware, software and overall system design. After years of emphasizing power reduction, performance has re-emerged as a top concern in a variety of applications such as smarter cars, wearable devices and cloud data centers. But how to get there has cha... » read more

Looking Beyond Technology

The semiconductor industry is beginning to make real progress in deep learning and artificial intelligence, opening up bigger opportunities across more markets than have ever existed in the history of technology. But before this revolution goes much further, the industry also needs to step back and establish a set of guidelines about how this technology will be used. This is an entirely dif... » read more

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