Deep Learning for Computer Architects (Synthesis Lectures on Computer Architecture)

Paul Whatmough, Gu-Yeon Wei, David Brooks

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商品描述

Machine learning, and specifically deep learning, has been hugely disruptive in many fields of computer science. The success of deep learning techniques in solving notoriously difficult classification and regression problems has resulted in their rapid adoption in solving real-world problems. The emergence of deep learning is widely attributed to a virtuous cycle whereby fundamental advancements in training deeper models were enabled by the availability of massive datasets and high-performance computer hardware.

This text serves as a primer for computer architects in a new and rapidly evolving field. We review how machine learning has evolved since its inception in the 1960s and track the key developments leading up to the emergence of the powerful deep learning techniques that emerged in the last decade. Next we review representative workloads, including the most commonly used datasets and seminal networks across a variety of domains. In addition to discussing the workloads themselves, we also detail the most popular deep learning tools and show how aspiring practitioners can use the tools with the workloads to characterize and optimize DNNs.

The remainder of the book is dedicated to the design and optimization of hardware and architectures for machine learning. As high-performance hardware was so instrumental in the success of machine learning becoming a practical solution, this chapter recounts a variety of optimizations proposed recently to further improve future designs. Finally, we present a review of recent research published in the area as well as a taxonomy to help readers understand how various contributions fall in context.

商品描述(中文翻譯)

機器學習,尤其是深度學習,在許多計算機科學領域中帶來了巨大的顛覆性影響。深度學習技術在解決聞名難題的分類和回歸問題方面的成功,導致它們在解決現實世界問題時迅速被廣泛採用。深度學習的出現被廣泛歸因於一個良性循環,即基於大規模數據集和高性能計算機硬件的可用性,使得訓練更深層模型的基礎進展成為可能。

本書旨在為計算機架構師提供一個新興且快速發展的領域的入門指南。我們回顧了自1960年代以來機器學習的演變,並追蹤了導致在過去十年中出現強大的深度學習技術的關鍵發展。接下來,我們回顧了代表性的工作負載,包括各個領域中最常用的數據集和開創性的網絡。除了討論工作負載本身外,我們還詳細介紹了最流行的深度學習工具,並展示了如何使用這些工具與工作負載一起對深度神經網絡進行特徵化和優化。

本書的其餘部分專門介紹了機器學習的硬件和架構設計以及優化。由於高性能硬件在機器學習成功成為實際解決方案中起到了重要作用,本章回顧了最近提出的各種優化方法,以進一步改進未來的設計。最後,我們提供了對該領域最近發表的研究的回顧,並提供了一個分類法,幫助讀者理解各種貢獻的背景。