Efficient Processing of Deep Neural Networks (Paperback)

Sze, Vivienne, Chen, Yu-Hsin, Yang, Tien-Ju

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商品描述

This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs). DNNs are currently widely used for many artificial intelligence (AI) applications, including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Therefore, techniques that enable efficient processing of deep neural networks to improve key metrics--such as energy-efficiency, throughput, and latency--without sacrificing accuracy or increasing hardware costs are critical to enabling the wide deployment of DNNs in AI systems.

The book includes background on DNN processing; a description and taxonomy of hardware architectural approaches for designing DNN accelerators; key metrics for evaluating and comparing different designs; features of DNN processing that are amenable to hardware/algorithm co-design to improve energy efficiency and throughput; and opportunities for applying new technologies. Readers will find a structured introduction to the field as well as formalization and organization of key concepts from contemporary work that provide insights that may spark new ideas.

商品描述(中文翻譯)

本書提供了一個結構化的方法,介紹了實現深度神經網絡(DNNs)高效處理的關鍵原則和技術。DNNs目前廣泛應用於許多人工智能(AI)應用中,包括計算機視覺、語音識別和機器人技術。儘管DNNs在許多AI任務上提供了最先進的準確性,但這是以高計算複雜性為代價的。因此,實現對DNNs進行高效處理的技術,以提高能源效率、吞吐量和延遲等關鍵指標,同時不損失準確性或增加硬件成本,對於在AI系統中廣泛部署DNNs至關重要。

本書包括DNN處理的背景知識;描述和分類設計DNN加速器的硬件架構方法;評估和比較不同設計的關鍵指標;適合進行硬件/算法協同設計以提高能源效率和吞吐量的DNN處理特性;以及應用新技術的機會。讀者將在本書中找到對該領域的結構化介紹,以及對當代工作中的關鍵概念的形式化和組織,這些概念提供了可能激發新思路的見解。