Midlands State University Library
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Hardware accelerator systems for artificial intelligence and machine learning / created by Shiho Kim and Ganesh Chandra Deka

By: Contributor(s): Material type: TextTextSeries: Advances in computersPublisher: Academic Press, an imprint of Elsevier, 2021Copyright date: ©2021Description: xii, 402 pages : illustrations ; 24 cmContent type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 9780128231234
Subject(s): LOC classification:
  • QA76.9.B56 HAR
Contents:
Introduction 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
Summary: Hardware 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
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Introduction 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

Hardware 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

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