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A Guide to Processors for Deep Learning

First Edition

To be Published September 2017
Order by September 30 and save $300

Authors: Linley Gwennap, Mike Demler, and Loyd Case

Single License: $4,495 (single copy, one user)
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 starting to move into client devices such as smartphones and embedded (IoT) systems.

Even the fastest CPUs are inadequate for running the highly complex neural networks needed to address these advanced problems. Boosting performance requires more specialized hardware architectures. Graphics chips (GPUs), which are more powerful and more efficient than CPUs for deep learning, 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 racing to tune 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 are leading the way, investing heavily in new processors for deep learning. Many startups, some well funded, have emerged to develop new, more customized architectures for deep learning. Some focus on training, others on inference. Eschewing these options, leading data-center operators such as Google and Microsoft have developed their own hardware accelerators. In addition, several IP vendors offer specialized cores for deep learning, mainly for inference in autonomous vehicles and other client devices.

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. 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, Intel (including former Altera, Mobileye, Movidius, and Nervana technologies), NXP, Nvidia (including Tegra and Tesla), Qualcomm, Wave Computing, and Xilinx. It also covers IP cores from AImotive, ARM, Cadence, Ceva, Imagination, Synopsys, and VeriSilicon. A special chapter covers Google’s TPU and TPU2 ASICs. 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 analysts Mike Demler and Loyd Case 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 or IP for an automotive application 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!

What's New in This Edition

The first edition of A Guide to Processors for Deep Learning is completely new. Highlights include:

  • Nvidia’s new Tesla V100 (Volta) accelerator for deep learning
  • Cadence’s first IP core optimized for neural networks, the Vision C5
  • How Intel’s acquisition of Mobileye affects its autonomous-driving roadmap
  • Nvidia’s new Xavier chip for autonomous cars
  • Intel’s newest Xeon Phi processor (code-named Knights Landing)
  • AMD’s new Radeon Instinct accelerators for deep learning
  • Intel’s integration of Nervana technology into its deep-learning roadmap
  • Applying the Xilinx Virtex FPGAs to neural networks
  • Ceva’s next-generation XM6 core for deep learning
  • How Intel’s Stratix 10 FPGA can execute deep-learning algorithms
  • Wave Computing’s DPU, designed specifically for deep learning
  • VeriSilicon’s VIP8000-O, its first IP core for neural networks

Coming soon.


Linley Processor Conference 2017
Covers processors and IP cores used in deep learning, embedded, communications, automotive, IoT, and server designs.
October 4 - 5, 2017
Hyatt Regency, Santa Clara, CA
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