Towards Heterogeneous Multi-core Systems-on-Chip for Edge Machine Learning
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Towards Heterogeneous Multi-core Systems-on-Chip for Edge Machine Learning
Journey from Single-core Acceleration to Multi-core Heterogeneous Systems
Verhelst, Marian; Jain, Vikram
Springer International Publishing AG
09/2024
186
Mole
9783031382321
15 a 20 dias
Descrição não disponível.
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.
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Edge AI;machine learning;hardware accelerators;homogeneous and heterogeneous systems;deep learning
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.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
