Artificial Intelligence Hardware Design

<b>ARTIFICIAL INTELLIGENCE HARDWARE DESIGN</b> <p><b>Learn foundational and advanced topics in Neural Processing Unit design with real-world examples from leading voices in the field</b> <p>In <i>Artificial Intelligence Hardware Design: Challenges and Solutions</i>, distinguished researchers and authors Drs. Albert Chun Chen Liu and Oscar Ming Kin Law deliver a rigorous and practical treatment of the design applications of specific circuits and systems for accelerating neural network processing. Beginning with a discussion and explanation of neural networks and their developmental history, the book goes on to describe parallel architectures, streaming graphs for massive parallel computation, and convolution optimization. <p>The authors offer readers an illustration of in-memory computation through Georgia Tech’s Neurocube and Stanford’s Tetris accelerator using the Hybrid Memory Cube, as well as near-memory architecture through the embedded eDRAM of the Institute of Computing Technology, the Chinese Academy of Science, and other institutions. <p>Readers will also find a discussion of 3D neural processing techniques to support multiple layer neural networks, as well as information like: <ul><li>A thorough introduction to neural networks and neural network development history, as well as Convolutional Neural Network (CNN) models</li> <li>Explorations of various parallel architectures, including the Intel CPU, Nvidia GPU, Google TPU, and Microsoft NPU, emphasizing hardware and software integration for performance improvement</li> <li>Discussions of streaming graph for massive parallel computation with the Blaize GSP and Graphcore IPU</li> <li>An examination of how to optimize convolution with UCLA Deep Convolutional Neural Network accelerator filter decomposition</li></ul> <p>Perfect for hardware and software engineers and firmware developers, <i>Artificial Intelligence Hardware Design</i> is an indispensable resource for anyone working with Neural Processing Units in either a hardware or software capacity.