Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing

Chapter 1 Massively Parallel Neural Processing Array (MPNA): A CNN Accelerator for Embedded Systems

Chapter 2 Photonic NoCs for Energy-Efficient Data-Centric Computing

Chapter 3 Low- and Mixed-Precision Inference Accelerators

Chapter 4 Designing Resource-Efficient Hardware Arithmetic for FPGA-Based Accelerators Leveraging Approximations and Mixed Quantizations

Chapter 5 Efficient Hardware Acceleration of Emerging Neural Networks for Embedded Machine Learning: An Industry Perspective

Chapter 6 An Off-Chip Memory Access Optimization for Embedded Deep Learning Systems

Chapter 7 In-Memory Computing for AI Accelerators: Challenges and Solutions

Chapter 8 Efficient Deep Learning Using Non-volatile Memory Technology in GPU Architectures

Chapter 9 SoC-GANs: Energy-Efficient Memory Management for System-on-Chip Generative Adversarial Networks

Chapter 10 Using Approximate DRAM for Enabling Energy-Efficient, High-Performance Deep Neural Network Inference

Chapter 11 On-Chip DNN Training for Direct Feedback Alignment in FeFET

Chapter 12 Platform-Based Design of Embedded Neuromorphic Systems

Chapter 13 Light Speed Machine Learning Inference on the Edge

Chapter 14 Low-Latency, Energy-Efficient In-DRAM CNN Acceleration with Bit-Parallel Unary Computing