tag: inference

Edge Inference Applications And Market Segmentation
By Geoff Tate - 03 Dec, 2020 - Comments: 0

Until recently, most AI was in data centers/cloud and most of that was training. Things are changing quickly. Projections are AI sales will grow rapidly to tens of billions of dollars by the mid 2020s, with most of the growth in edge AI inference. Data center/cloud vs. edge inference: What’s the difference? The data center/cloud is where inference started on Xeons. To gain efficiency, much ... » read more

Convolutional Neural Network With INT4 Optimization
By Xilinx - 01 Dec, 2020 - Comments: 0

Xilinx provides an INT8 AI inference accelerator on Xilinx hardware platforms — Deep Learning Processor Unit (XDPU). However, in some resource-limited, high-performance and low-latency scenarios (such as the resource-power-sensitive edge side and low-latency ADAS scenario), low bit quantization of neural networks is required to achieve lower power consumption and higher performance than provi... » read more

ResNet-50 Does Not Predict Inference Throughput For MegaPixel Neural Network Models
By Geoff Tate - 05 Nov, 2020 - Comments: 1

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
By Jesse Allen - 30 Oct, 2020 - Comments: 0

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
By Jesse Allen - 27 Oct, 2020 - Comments: 0

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
By Jesse Allen - 23 Oct, 2020 - Comments: 0

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
By Geoff Tate - 08 Oct, 2020 - Comments: 0

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
By Geoff Tate - 03 Sep, 2020 - Comments: 0

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
By Ed Sperling And Susan Rambo - 06 Aug, 2020 - Comments: 0

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?
By Bryon Moyer - 01 Jul, 2020 - Comments: 1

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

← Older posts Newer posts →

  • Sponsors

About

Navigation

Connect With Us

Copyright ©2013-2024 SMG   |  Terms of Service  |  Privacy Policy