TY  - BOOK
AU  - Jain,Vikram
AU  - Verhelst,Marian
ED  - SpringerLink (Online service)
TI  - Towards Heterogeneous Multi-core Systems-on-Chip for Edge Machine Learning: Journey from Single-core Acceleration to Multi-core Heterogeneous Systems 
SN  - 9783031382307
AV  - TK7867-7867.5 
U1  - 621.3815 23
PY  - 2024///
CY  - Cham
PB  - Springer Nature Switzerland, Imprint: Springer
KW  - Electronic circuits
KW  - Embedded computer systems
KW  - Machine learning
KW  - Microprocessors
KW  - Computer architecture
KW  - Electronic Circuits and Systems
KW  - Embedded Systems
KW  - Machine Learning
KW  - Processor Architectures
N1  - 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
N2  - 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.
UR  - http://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-3-031-38230-7
ER  -