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ORCIDAdvances in Computers, Volume 122
Volume 122, 2021
- Shiho Kim, Ganesh Chandra Deka:
Preface. xi-xii - Neha Gupta
:
Chapter One - Introduction to hardware accelerator systems for artificial intelligence and machine learning. 1-21 - William J. Song:
Chapter Two - Hardware accelerator systems for embedded systems. 23-49 - Hyunbin Park, Shiho Kim:
Chapter Three - Hardware accelerator systems for artificial intelligence and machine learning. 51-95 - Parth Bir:
Chapter Four - Generic quantum hardware accelerators for conventional systems. 97-133 - Joo-Young Kim:
Chapter Five - FPGA based neural network accelerators. 135-165 - Won Jeon, Gun Ko, Jiwon Lee, Hyunwuk Lee, Dongho Ha, Won Woo Ro:
Chapter Six - Deep learning with GPUs. 167-215 - Kyuho J. Lee:
Chapter Seven - Architecture of neural processing unit for deep neural networks. 217-245 - Francesco Daghero, Daniele Jahier Pagliari, Massimo Poncino:
Chapter Eight - Energy-efficient deep learning inference on edge devices. 247-301 - Yuri G. Gordienko, Yuriy Kochura, Vlad Taran, Nikita Gordienko, Oleksandr Rokovyi, Oleg Alienin, Sergii G. Stirenko:
Chapter Nine - "Last mile" optimization of edge computing ecosystem with deep learning models and specialized tensor processing architectures. 303-341 - Hyunbin Park, Shiho Kim:
Chapter Ten - Hardware accelerator for training with integer backpropagation and probabilistic weight update. 343-365 - Amitabh Biswal, Malaya Dutta Borah, Zakir Hussain:
Chapter Eleven - Music recommender system using restricted Boltzmann machine with implicit feedback. 367-402
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