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.
1. Chapter One: Introduction to hardware accelerator systems for artificial intelligence and machine learning - p. page_1 2. Chapter Two: Hardware accelerator systems for embedded systems - p. page_23 3. Chapter Three: Hardware accelerator systems for artificial intelligence and machine learning - p. page_51 4. Chapter Four: Generic quantum hardware accelerators for conventional systems - p. page_97 5. Chapter Five: FPGA based neural network accelerators - p. page_135 6. Chapter Six: Deep learning with GPUs - p. page_167 7. Chapter Seven: Architecture of neural processing unit for deep neural networks - p. page_217 8. Chapter Eight: Energy-efficient deep learning inference on edge devices - p. page_247 9. Chapter Nine: “Last mile” optimization of edge computing ecosystem with deep learning models and specialized tensor processing architectures - p. page_303 10. Chapter Ten: Hardware accelerator for training with integer backpropagation and probabilistic weight update - p. page_343 11. Chapter Eleven: Music recommender system using restricted Boltzmann machine with implicit feedback - p. page_367
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.
1. Chapter One: Introduction to hardware accelerator systems for artificial intelligence and machine learning - p. page_1 2. Chapter Two: Hardware accelerator systems for embedded systems - p. page_23 3. Chapter Three: Hardware accelerator systems for artificial intelligence and machine learning - p. page_51 4. Chapter Four: Generic quantum hardware accelerators for conventional systems - p. page_97 5. Chapter Five: FPGA based neural network accelerators - p. page_135 6. Chapter Six: Deep learning with GPUs - p. page_167 7. Chapter Seven: Architecture of neural processing unit for deep neural networks - p. page_217 8. Chapter Eight: Energy-efficient deep learning inference on edge devices - p. page_247 9. Chapter Nine: “Last mile” optimization of edge computing ecosystem with deep learning models and specialized tensor processing architectures - p. page_303 10. Chapter Ten: Hardware accelerator for training with integer backpropagation and probabilistic weight update - p. page_343 11. Chapter Eleven: Music recommender system using restricted Boltzmann machine with implicit feedback - p. page_367
