{"product_id":"9781119810452","title":"Artificial Intelligence Hardware Design Challenges and Solutions","description":"\u003ch1\u003eArtificial Intelligence Hardware Design\u003c\/h1\u003e\u003ch2\u003eChallenges and Solutions\u003c\/h2\u003e\u003ch3\u003eAlbert Chun-Chen Liu | Oscar Ming Kin Law\u003c\/h3\u003e\u003cdiv\u003e\u003cb\u003eComputers \/ Data Science \/ Neural Networks\u003c\/b\u003e\u003c\/div\u003e\u003cbr\u003e\u003cdiv\u003e\n\u003cb\u003eARTIFICIAL INTELLIGENCE HARDWARE DESIGN\u003c\/b\u003e \u003cp\u003e\u003cb\u003eLearn foundational and advanced topics in Neural Processing Unit design with real-world examples from leading voices in the field\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eIn \u003ci\u003eArtificial Intelligence Hardware Design: Challenges and Solutions\u003c\/i\u003e, 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. \u003c\/p\u003e\n\u003cp\u003eThe 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. \u003c\/p\u003e\n\u003cp\u003eReaders will also find a discussion of 3D neural processing techniques to support multiple layer neural networks, as well as information like: \u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eA thorough introduction to neural networks and neural network development history, as well as Convolutional Neural Network (CNN) models\u003c\/li\u003e \u003cli\u003eExplorations of various parallel architectures, including the Intel CPU, Nvidia GPU, Google TPU, and Microsoft NPU, emphasizing hardware and software integration for performance improvement\u003c\/li\u003e \u003cli\u003eDiscussions of streaming graph for massive parallel computation with the Blaize GSP and Graphcore IPU\u003c\/li\u003e \u003cli\u003eAn examination of how to optimize convolution with UCLA Deep Convolutional Neural Network accelerator filter decomposition\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003ePerfect for hardware and software engineers and firmware developers, \u003ci\u003eArtificial Intelligence Hardware Design\u003c\/i\u003e is an indispensable resource for anyone working with Neural Processing Units in either a hardware or software capacity.\u003c\/p\u003e\n\u003c\/div\u003e\u003cdiv\u003e \u003cp\u003e\u003cb\u003eAlbert Chun Chen Liu, PhD,\u003c\/b\u003e is Chief Executive Officer of Kneron. He is Adjunct Associate Professor at National Tsing Hua University, National Chiao Tung University, and National Cheng Kung University. He has published over 15 IEEE papers and is an IEEE Senior Member. He is a recipient of the IBM Problem Solving Award based on the use of the EIP tool suite in 2007 and IEEE TCAS Darlington award in 2021.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eOscar Ming Kin Law, PhD,\u003c\/b\u003e is the Director of Engineering at Kneron. He works on smart robot development and in-memory architecture for neural networks. He has over twenty years of experience in the semiconductor industry working with CPU, GPU, and mobile design. He has also published over 60 patents in various areas. \u003c\/p\u003e\n\u003c\/div\u003e\u003cbr\u003e\u003ctable\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublication Date: \u003c\/td\u003e\n\u003ctd\u003e31 August 2021\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublisher: \u003c\/td\u003e\n\u003ctd\u003eWiley\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eImprint: \u003c\/td\u003e\n\u003ctd\u003eWiley-IEEE Press\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eISBN-13: \u003c\/td\u003e\n\u003ctd\u003e9781119810452\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFormat: \u003c\/td\u003e\n\u003ctd\u003eHardback\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePage Count: \u003c\/td\u003e\n\u003ctd\u003e240\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWeight (oz): \u003c\/td\u003e\n\u003ctd\u003e16.0\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":44384460472460,"sku":"9781119810452","price":110.66,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0710\/9545\/1788\/files\/9781119810452.jpg?v=1780179631","url":"https:\/\/lateknightbooks.com\/products\/9781119810452","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}