# Efficient Processing of Deep Neural Networks

Source: https://listme.name/books/efficient-processing-of-deep-neural-networks
Website: https://link.springer.com/book/10.1007/978-3-031-01766-7

Hardware-focused book on running deep neural networks efficiently

Efficient Processing of Deep Neural Networks is a 2020 Springer book by Vivienne Sze, Yu-Hsin Chen, Tien-Ju Yang and Joel S. Emer. It covers how to run deep neural networks efficiently, with trade-offs between hardware design and algorithm choices. Springer sells an eBook in PDF and a softcover print edition.

The book has 11 chapters, grouped into an introduction to deep neural networks, the design of hardware for processing them, and the co-design of hardware and algorithms. It is aimed at engineers who build accelerators and at researchers who need to understand the energy and speed costs of models.

The publisher page lists the book as a paid eBook in PDF and as a softcover, and it does not offer open access. The authors are from MIT and NVIDIA Research. The book is more hardware-oriented than the reinforcement learning texts in this list, so it fits readers who run models on constrained chips rather than train them.

- Paid | PDF/Print
- Price: eBook in PDF and softcover from Springer; no free full text
- Checked: 2026-10-10

## Strengths

- Covers hardware and algorithm trade-offs together
- Structured in 11 chapters
- Written by hardware and efficient computing researchers

## Limits

- Paid, with no free full text on the publisher page
- Not about reinforcement learning

## Rankings

- #6 of 10 in [best Reinforcement learning and edge ML books](https://listme.name/best/reinforcement-learning-and-edge-ml-books), score 87.5: A structured guide to hardware and algorithm choices for efficient deep neural network processing, from a 2020 Springer book. It is paid, has no free text, and covers no reinforcement learning.

## Alternatives

- [Reinforcement Learning: An Introduction](https://listme.name/books/reinforcement-learning-an-introduction), score 94.0
- [Reinforcement Learning and Optimal Control](https://listme.name/books/reinforcement-learning-and-optimal-control), score 90.5
- [TinyML](https://listme.name/books/tinyml-book), score 89.0
- [Bandit Algorithms](https://listme.name/books/bandit-algorithms), score 88.0
- [Reinforcement Learning: Theory and Algorithms](https://listme.name/books/reinforcement-learning-theory-and-algorithms), score 86.0
- [Foundations of Deep Reinforcement Learning](https://listme.name/books/foundations-of-deep-reinforcement-learning), score 85.0
- [Algorithms for Reinforcement Learning](https://listme.name/books/algorithms-for-reinforcement-learning), score 84.5
- [Fringe Learning: Resource-Efficient RL for Edge ML](https://listme.name/books/fringe-learning), score 79.5