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Hardware Accelerator Systems for Artificial Intelligence and Machine Learning
Explores research trends in the architecture of GPU, DNN and NPU
Shiho Kim (Volume editor), Ganesh Chandra Deka (Volume editor)
9780128231234
Hardback, published 7 April 2021
416 pages
22.9 x 15.2 x 2.7 cm, 0.81 kg
Hardware Accelerator Systems for Artificial Intelligence and Machine Learning, Volume 122 delves into arti?cial 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.
1. Hardware accelerator systems for artificial intelligence and machine learning Shiho Kim 2. Introduction to Hardware Accelerator Systems for Artificial Intelligence and Machine Learning Neha Gupta 3. Deep Learning with GPUs Won Woo Ro 4. Edge Computing Optimization of Deep Learning Models for Specialized Tensor Processing Architectures-Yuri Gordienko Yuri Gordienko 5. Architecture of NPU for DNN Kyuho Lee 6. Hardware Architecture for Convolutional Neural Network for Image Processing Vardhana M 7. FPGA based Neural Network Accelerators Joo-Young Kim 8. Energy-Efficient Deep Learning Inference on Edge Devices Massimo Poncino 9. Hardware accelerator systems for Embedded systems William Jinho Song 10. Generic Quantum Hardware Accelerators for Conventional systems Parth Bir 11. Music recommender system using Restricted Boltzmann Machine with Implicit Feedback Malaya Dutta Borah 12. Embedded system for Automated Monitoring in Agriculture and Healthcare Prashanta Kumar Das
