000			02943nam a22003017a 4500
003			ZW-GwMSU
005			20240506104054.0
008			240506b |||||||| |||| 00| 0 eng d
020			_a9780128231234
040			_arda _bEnglish _cMSULIB _erda
050	0	0	_aQA76.9.B56 HAR
100	1		_aKim, Shiho _eEditor
245	1	0	_aHardware accelerator systems for artificial intelligence and machine learning / _ccreated by Shiho Kim and Ganesh Chandra Deka
264		1	_bAcademic Press, _ban imprint of Elsevier, _c2021
264		4	_c©2021
300			_axii, 402 pages : _billustrations ; _c24 cm
336			_2rdacontent _atext
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnc
490			_aAdvances in computers, _x122
505			_aIntroduction to hardware accelerator systems for artificial intelligence and machine learning / Neha Gupta Hardware accelerator systems for embedded systems / William J. Song Hardware accelerator systems for artificial intelligence and machine learning / Hyunbin Park and Shiho Kim Generic quantum hardware accelerators for conventional systems / Parth Bir FPGA based neural network accelerators / Joo-Young Kim Deep Learning with GPUs / Won Jeon, Gun Ko, Jiwon Lee, Hyunwuk Lee, Dongho Ha, and Won Woo Ro Architecture of neural processing unit for deep neural networks / Kyuho J. Lee Energy-Efficient Deep Learning Inference on Edge Devices / Francesco Daghero, Daniele Jahier Pagliari, and Massimo Poncino "Last mile" optimization of edge computing ecosystem with deep learning models and specialized tensor processing architectures / Yuri Gordienko, Yuriy Kocccchura, Vlad Taran, Nikita Gordienko, Oleksandr Rokovyi, Oleg Alienin, and Sergii Stirenko Hardware accelerator for training with integer backpropagation and probabilistic weight update / Hyunbin Park and Shiho Kim Music recommender system using restricted Boltzmann machine with implicit feedback / Amitabh Biswal, Malaya Dutta Borah, and Zakir Hussain
520			_aHardware Accelerator Systems for Artificial Intelligence and Machine Learning, Volume 122 delves into artificial Intelligence and the growth it has seen with the advent of Deep Neural Networks (DNNs) and Machine Learning. Updates in this release include chapters on Hardware accelerator systems for artificial intelligence and machine learning, Introduction to Hardware Accelerator Systems for Artificial Intelligence and Machine Learning, Deep Learning with GPUs, Edge Computing Optimization of Deep Learning Models for Specialized Tensor Processing Architectures, Architecture of NPU for DNN, Hardware Architecture for Convolutional Neural Network for Image Processing, FPGA based Neural Network Accelerators, and much more--Publisher's description
650		0	_aComputer Systems
650		0	_aArtificial Intelligence
650		0	_aMachine learning
700			_aDeka ,Ganesh Chandra _eEditor
942			_2lcc _cB
999			_c165281 _d165281