Efficient Processing of Deep Neural Networks

Deep-learning models can deliver impressive results, but running them efficiently is a demanding computer-architecture problem. Efficient Processing of Deep Neural Networks explains the hardware and algorithmic ideas that help address that challenge, with attention to energy efficiency, throughput, latency, accuracy, and hardware cost.

Vivienne Sze, Yu-Hsin Chen, Tien-Ju Yang, and Joel S. Emer present a structured introduction to DNN processing and the architectures built to accelerate it. The discussion is useful for understanding not only what accelerator designs do, but also how to compare them and where hardware and algorithm design can work together.

How DNN Accelerators Are Designed

The book describes and organizes architectural approaches for designing hardware accelerators for deep neural networks. This framework gives readers a way to make sense of different designs and the choices behind them.

Compare Designs by What Matters

Evaluating an accelerator means looking beyond a single performance figure. The authors discuss key metrics for comparing designs, including energy efficiency, throughput, and latency—considerations that shape how DNNs can be deployed in real systems.

Where Hardware and Algorithms Meet

A central theme is hardware-and-algorithm co-design. By identifying features of DNN processing that can be addressed jointly, the book explores ways to improve efficiency and throughput while keeping accuracy and hardware costs in view.

For Readers of Architecture and AI Systems

This volume will interest readers studying computer architecture, deep-learning systems, and accelerator design, as well as practitioners seeking a structured view of the field’s key concepts. It connects neural-network processing to the design decisions that determine how efficiently AI workloads run.

For a considered introduction to the architecture behind efficient deep learning, this book offers a clear map of the challenges, measures, and design approaches involved.