000			a
999			_c33815 _d33815
008			250316b xxu||||| |||| 00| 0 eng d
020			_a9781119810452 _c(hbk)
082			_a006.3 _bLIU
100			_aLiu, Albert Chun-Chen
245			_aArtificial intelligence hardware design : challenges and solutions
260			_bWiley-IEEE Press, _c2021 _aHoboken :
300			_axxii, 208 p. ; _bill.,(b & w), _c24 cm.
365			_b118.95 _c$ _d90.60
504			_aIncludes bibliographical references and index.
520			_aARTIFICIAL INTELLIGENCE HARDWARE DESIGN Learn foundational and advanced topics in Neural Processing Unit design with real-world examples from leading voices in the field In Artificial Intelligence Hardware Design: Challenges and Solutions , 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. 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. Readers will also find a discussion of 3D neural processing techniques to support multiple layer neural networks, as well as information like: A thorough introduction to neural networks and neural network development history, as well as Convolutional Neural Network (CNN) models Explorations of various parallel architectures, including the Intel CPU, Nvidia GPU, Google TPU, and Microsoft NPU, emphasizing hardware and software integration for performance improvement Discussions of streaming graph for massive parallel computation with the Blaize GSP and Graphcore IPU An examination of how to optimize convolution with UCLA Deep Convolutional Neural Network accelerator filter decomposition Perfect for hardware and software engineers and firmware developers, Artificial Intelligence Hardware Design is an indispensable resource for anyone working with Neural Processing Units in either a hardware or software capacity.
650			_aArtificial Intelligence
650			_aComputer Design and Construction
650			_aParallel architectures
650			_aStreaming graphs
650			_aConvolution optimization
650			_aIn-memory computation
650			_aNea-memory Architecture
650			_aNetwork Sparsity
650			_a3D neural processing
700			_aLaw, Oscar Ming Kin
942			_2ddc _cBK