Software-Defined Hardware Gains Ground — Again


The traditional approach of running generic software on x86-based CPUs is running out of steam for many applications due to the slowdown of Moore’s Law and the concurrent exponential growth in software application complexity and scale. In this environment, the software and hardware are disparate due the dominance of the x86 architecture. “The need for and advent of the hardware accelerat... » read more

Machine Learning At The Edge


Moving machine learning to the edge has critical requirements on power and performance. Using off-the-shelf solutions is not practical. CPUs are too slow, GPUs/TPUs are expensive and consume too much power, and even generic machine learning accelerators can be overbuilt and are not optimal for power. In this paper, learn about creating new power/memory efficient hardware architectures to meet n... » read more

An Increasingly Complicated Relationship With Memory


The relationship between a processor and its memory used to be quite simple, but in modern SoCs there are multiple heterogeneous processors and accelerators, each needing a different means of accessing memory for maximum efficiency. Compromises are being made in order to preserve the unified programming model of the past, but the pressures are increasing for some fundamental changes. It does... » read more

Solving The Memory Bottleneck


Chipmakers are scrambling to solve the bottleneck between processor and memory, and they are turning out new designs based on different architectures at a rate no one would have anticipated even several months ago. At issue is how to boost performance in systems, particularly those at the edge, where huge amounts of data need to be processed locally or regionally. The traditional approach ha... » read more

Why Scaling Must Continue


The entire semiconductor industry has come to the realization that the economics of scaling logic are gone. By any metric—price per transistor, price per watt, price per unit area of silicon—the economics are no longer in the plus column. So why continue? The answer is more complicated than it first appears. This isn't just about inertia and continuing to miniaturize what was proven in t... » read more

Optimizing Power For Learning At The Edge


Learning on the edge is seen as one of the Holy Grails of machine learning, but today even the cloud is struggling to get computation done using reasonable amounts of power. Power is the great enabler—or limiter—of the technology, and the industry is beginning to respond. "Power is like an inverse pyramid problem," says Johannes Stahl, senior director of product marketing at Synopsys. "T... » read more

Inferencing Efficiency


Geoff Tate, CEO of Flex Logix, talks with Semiconductor Engineering about how to measure efficiency in inferencing chips, how to achieve the most throughput for the lowest cost, and what the benchmarks really show. » read more

CEO Outlook: Rising Costs, Chiplets, And A Trade War


Semiconductor Engineering sat down to discuss what's changing across the semiconductor industry with Wally Rhines, CEO emeritus at Mentor, a Siemens Business; Jack Harding, president and CEO of eSilicon; John Kibarian, president and CEO of PDF Solutions; and John Chong, vice president of product and business development for Kionix. What follows are excerpts of that discussion, which was held in... » read more

Spreading Intelligence From The Cloud To The Edge


The challenge of partitioning processing between the edge and the cloud is beginning to come into focus as chipmakers and systems companies wrestle with a massive and rapidly growing volume of data. There are widely different assessments of how much data this ultimately will include, but everyone agrees it is a very large number. Petabytes are simply rounding errors in this equation, and tha... » read more

AI Chip Architectures Race To The Edge


As machine-learning apps start showing up in endpoint devices and along the network edge of the IoT, the accelerators that make AI possible may look more like FPGA and SoC modules than current data-center-bound chips from Intel or Nvidia. Artificial intelligence and machine learning need powerful chips for computing answers (inference) from large data sets (training). Most AI chips—both tr... » read more

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