Chapter 1: Introduction -- Chapter 2 Algorithmic Background for Machine Learning -- Chapter 3 Scoping the Landscape of (Extreme) Edge Machine Learning Processors -- Chapter 4 Hardware-Software Co-optimization through Design Space Exploration -- Chapter 5 Energy Efficient Single-core Hardware Acceleration -- Chapter 6 TinyVers: A Tiny Versatile All-Digital Heterogeneous Multi-core System-on-Chip -- Chapter 7 DIANA: Digital and ANAlog Heterogeneous Multi-core System-on-Chip -- Chapter 8 Networks-on-chip to Enable Large-scale Multi-core ML Acceleration -- Chapter 9 Conclusion.

This book explores and motivates the need for building homogeneous and heterogeneous multi-core systems for machine learning to enable flexibility and energy-efficiency. Coverage focuses on a key aspect of the challenges of (extreme-)edge-computing, i.e., design of energy-efficient and flexible hardware architectures, and hardware-software co-optimization strategies to enable early design space exploration of hardware architectures. The authors investigate possible design solutions for building single-core specialized hardware accelerators for machine learning and motivates the need for building homogeneous and heterogeneous multi-core systems to enable flexibility and energy-efficiency. The advantages of scaling to heterogeneous multi-core systems are shown through the implementation of multiple test chips and architectural optimizations. Discusses the need for scaling to multi-core systems for machine learning and several architectural and software optimizations; Covers single-core, homogeneous and heterogeneous multi-core Systems-on-chip for machine learning applications; Discusses the benefits of heterogeneity in the context of machine learning. .