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A Guide to Processors for Deep Learning

Fourth Edition

To be Published February 2021
Order by January 31 and save $500

Authors: Linley Gwennap and Mike Demler

Corporate License: $5,995

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Take a Deep Dive into Deep Learning

Deep learning, also known as artificial intelligence (AI), has seen rapid changes and improvements over the past few years and is now being applied to a wide variety of applications. Typically implemented using neural networks, deep learning powers image recognition, voice processing, language translation, and many other web services in large data centers. It is an essential technology in self-driving cars, providing both object recognition and decision making. It is even used in smartphones, PCs, and embedded (IoT) systems.

Even the fastest CPUs are inadequate to efficiently execute the highly complex neural networks needed to address these advanced problems. Boosting performance requires more specialized hardware architectures. Graphics chips (GPUs) have become popular, particularly for the initial training function. Many other hardware approaches have recently emerged, including DSPs, FPGAs, and dedicated ASICs. Although these solutions promise order-of-magnitude improvements, GPU vendors are tuning their designs to better support deep learning.

Autonomous vehicles are an important application for deep learning. Vehicles don't implement training but instead focus on the simpler inference tasks. Even so, these vehicles require very powerful processors, but they are more constrained in cost and power than data-center servers, requiring different tradeoffs. Several chip vendors are delivering products specifically for this application; some automakers are developing their own ASICs instead.

Large chip vendors such as Intel and Nvidia currently generate the most revenue from deep-learning processors. But many startups, some well funded, have emerged to develop new, more customized architectures for deep learning; Cerebras, Graphcore, Greenwaves, Gyrfalcon, Groq, Horizon Robotics, Tenstorrent, and Untether are among the first to deliver products. Eschewing these options, leading data-center operators such as Alibaba, Amazon, and Google have developed their own hardware accelerators.

We Sort Out the Market and the Products

A Guide to Processors for Deep Learning covers hardware technologies and products. The report provides deep technology analysis and head-to-head product comparisons, as well as analysis of company prospects in this rapidly developing market segment. We explain which products will win designs, and why. The Linley Group’s unique technology analysis provides a forward-looking view, helping sort through competing claims and products.

The guide begins with a detailed overview of the market. We explain the basics of deep learning, the types of hardware acceleration, and the end markets, including a forecast for both automotive and data-center adoption. The heart of the report provides detailed technical coverage of announced chip products from AMD, Cambricon, Cerebras, Graphcore, Groq, Intel (including former Altera, Habana, Mobileye, and Movidius), Mythic, Nvidia (including Tegra and Tesla), NXP, and Xilinx. Other chapters cover Google’s TPU family of ASICs and Tesla’s autonomous-driving ASIC. We also include shorter profiles of numerous other companies developing AI chips of all sorts, including Amazon, Brainchip, Gyrfalcon, Hailo, Huawei, Lattice, Qualcomm, Synaptics, and Texas Instruments. Finally, we bring it all together with technical comparisons in each product category and our analysis and conclusions about this emerging market.

Make Informed Decisions

As the leading vendor of technology analysis for processors, The Linley Group has the expertise to deliver a comprehensive look at the full range of chips designed for a broad range of deep-learning applications. Principal analyst Linley Gwennap and senior analyst Mike Demler use their experience to deliver the deep technical analysis and strategic information you need to make informed business decisions.

Whether you are looking for the right processor for an automotive application, an IoT device, or a data-center accelerator, or seeking to partner with or invest in one of these vendors, this report will cut your research time and save you money. Make the smart decision: order A Guide to Processors for Deep Learning today.

This report is written for:

  • Engineers designing chips or systems for deep learning or autonomous vehicles
  • Marketing and engineering staff at companies that sell related chips who need more information on processors for deep learning or autonomous vehicles
  • Technology professionals who wish an introduction to deep learning, vision processing, or autonomous-driving systems
  • Financial analysts who desire a hype-free analysis of deep-learning processors and of which chip suppliers are most likely to succeed
  • Press and public-relations professionals who need to get up to speed on this emerging technology

This market is developing rapidly — don't be left behind!

The fourth edition of A Guide to Processors for Deep Learning covers dozens of new products and technologies announced in the past year, including:

  • The innovation behind Nvidia’s Ampere A100, the industry-leading GPU
  • The first products in Qualcomm’s power-efficient Cloud AI 100 line
  • Graphcore’s second-generation accelerator, the GC2000
  • Tenstorrent’s initial Grayskull product, which outperforms Nvidia’s T4 at the same 75W
  • Intel’s new Stratix NX, its first AI-optimized FPGA
  • AMD’s powerful new MI100 (CDNA) accelerator for supercomputers
  • NXP’s i.MX8M Plus, the company’s first microcontroller with AI acceleration
  • Untether’s TsunAImi, which packs an industry-leading 2,000 TOPS into a single card
  • The evolution of Google’s TPU family, including its next-generation TPUv4
  • Intel’s new Xe GPU initiative
  • Esperanto’s first product, which features more than 1,000 Minion cores at only 20W
  • New card and system products from Groq
  • A preview of the second-generation Wafer-Scale Engine (WSE2) from Cerebras
  • Updated roadmaps for Intel’s Habana accelerators and Agilex FPGAs
  • The Jacinto processor from Texas Instruments for Level 3 ADAS
  • Synaptics’ VS680, a low-cost SoC for AI-enabled consumer devices
  • Other new AI vendors such as Ambient, Aspinity, Coherent Logix, Kneron, Perceive, SambaNova, Sima, and XMOS
  • New product roadmaps and other updates on all vendors
  • Updated market size and forecast to include economic effects of 2020 pandemic
Preliminary Table of Contents
List of Figures
List of Tables
About the Authors
About the Publisher
Preface
Executive Summary
1 Deep-Learning Applications
2 Deep-Learning Technology
3 Deep-Learning Accelerators
4 Market Forecast
5 AMD
6 Cambricon
7 Cerebras
8 Google
9 Graphcore
10 Groq
11 Huawei
12 Intel
13 Mobileye (Intel)
14 Mythic
15 Nvidia Tegra
16 Nvidia Tesla
17 NXP
18 Tesla (Motors)
19 Xilinx
20 Other Automotive Vendors
Black Sesame
Blaize/ThinCL
Hailo
Horizon Robotics
Renesas
Toshiba
Texas Instruments
21 Other Data Center Vendors
Achronix
Alibaba
Amazon
Baidu
Centaur
Esperanto
Furiosa
Marvell
Microsoft
Qualcomm
SambaNova
22 Other Embedded Vendors
Ambient
Aspinity
BrainChip
Coherent Logix
Cornami
Eta Compute
Flex Logix
GrAI Matter
GreenWaves
Gyrfalcon
Kneron
Knowles
Lattice
NovuMind
Perceive
Sima
Synaptics
Syntiant
XMOS
23 Comparisons
24 Conclusions
Appendix: Further Reading
Index
This is a preliminary table of contents and is subject to change.

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