Deep Learning Hardware Design

This book describes deep learning systems: the algorithms, compilers, and processor components to efficiently train and deploy deep learning models for commercial applications.
The exponential growth in computational power is slowing at a time when the amount of compute consumed by state-of-the-art deep learning (DL) workloads is rapidly growing. Model size, serving latency, and power constraints are a significant challenge in the deployment of DL models for many applications. Therefore, it is imperative to codesign algorithms, compilers, and hardware to accelerate advances in this field with holistic system-level and algorithm solutions that improve performance, power, and efficiency.

by Andres Rodriguez (Author)
This book describes deep learning systems: the algorithms, compilers, and processor components to efficiently train and deploy deep learning models for commercial applications

by Tushar Krishna (Author), Hyoukjun Kwon (Author), Angshuman Parashar (Author), Michael Pellauer (Author), Ananda Samajdar (Author)
This Synthesis Lecture focuses on techniques for efficient data orchestration within DNN accelerators

Deep learning added a huge boost to the already rapidly developing field of computer vision. With deep learning, a lot of new applications of computer vision techniques have been introduced and are now becoming parts of our everyday lives. These include face recognition and indexing, photo stylization or machine vision in self-driving cars.

by Wujie Zhang (Author)
This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs).

Embedded Systems, IoT, RTOS, Deep Learning, Linux and Raspberry PI, ESP32, Arduino