ResNet-50 Does Not Predict Inference Throughput For MegaPixel Neural Network Models


Customers are considering applications for AI inference and want to evaluate multiple inference accelerators. As we discussed last month, TOPS do NOT correlate with inference throughput and you should use real neural network models to benchmark accelerators. So is ResNet-50 a good benchmark for evaluating relative performance of inference accelerators? If your application is going to p... » read more

Week In Review: Design, Low Power


M&A AMD will acquire Xilinx for $35 billion in an all-stock deal. "Joining together with AMD will help accelerate growth in our data center business and enable us to pursue a broader customer base across more markets,” said Victor Peng, Xilinx president and CEO. The deal is expected to close by the end of 2021. The acquisition of the programmable logic giant will leave only a few purepla... » read more

Power/Performance Bits: Oct. 27


Room-temp superconductivity Researchers at the University of Rochester, University of Nevada Las Vegas, and Intel created a material with superconducting properties at room temperature, the first time this has been observed. The researchers combined hydrogen with carbon and sulfur to photochemically synthesize simple organic-derived carbonaceous sulfur hydride in a diamond anvil cell, which... » read more

Week In Review: Design, Low Power


M&A Microchip Technology acquired LegUp Computing, a provider of a high-level synthesis compiler that automatically generates high-performance FPGA hardware from software. The LegUp HLS tool will be used alongside Microchip’s VectorBlox Accelerator Software Design kit and VectorBlox Neural Networking IP generator to provide a complete front-end solution stack for C/C++ algorithm develope... » read more

One More Time: TOPS Do Not Predict Inference Throughput


Many times you’ll hear vendors talking about how many TOPS their chip has and imply that more TOPS means better inference performance. If you use TOPS to pick your AI inference chip, you will likely not be happy with what you get. Recently, Vivienne Sze, a professor at MIT, gave an excellent talk entitled “How to Evaluate Efficient Deep Neural Network Approaches.” Slides are also av... » read more

Apples, Oranges & The Optimal AI Inference Accelerator


There are a wide range of AI inference accelerators available and a wide range of applications for them. No AI inference accelerator will be optimal for every application. For example, a data center class accelerator almost certainly will be too big, burn too much power, and cost too much for most edge applications. And an accelerator optimal for key word recognition won’t have the capabil... » read more

Big Changes In AI Design


Semiconductor Engineering sat down to discuss AI and its move to the edge with Steven Woo, vice president of enterprise solutions technology and distinguished inventor at Rambus; Kris Ardis, executive director at Maxim Integrated; Steve Roddy, vice president of Arm's Products Learning Group; and Vinay Mehta, inference technical marketing manager at Flex Logix. What follows are excerpts of that ... » read more

Are Better Machine Training Approaches Ahead?


We live in a time of unparalleled use of machine learning (ML), but it relies on one approach to training the models that are implemented in artificial neural networks (ANNs) — so named because they’re not neuromorphic. But other training approaches, some of which are more biomimetic than others, are being developed. The big question remains whether any of them will become commercially viab... » read more

ML Opening New Doors For FPGAs


FPGAs have long been used in the early stages of any new digital technology, given their utility for prototyping and rapid evolution. But with machine learning, FPGAs are showing benefits beyond those of more conventional solutions. This opens up a hot new market for FPGAs, which traditionally have been hard to sustain in high-volume production due to pricing, and hard to use for battery-dri... » read more

Using ML In Manufacturing


How to prevent early life failures by applying machine learning to different use cases, and how to interpret models for different tradeoffs on reliability. Jeff David, vice president of AI solutions at PDF Solutions, digs down into how to utilize data to improve reliability. » read more

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