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Anglický jazyk
Towards Heterogeneous Multi-core Systems-on-Chip for Edge Machine Learning
Autor: Vikram Jain
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.,... Viac o knihe
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O knihe
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.
- Vydavateľstvo: Springer
- Rok vydania: 2023
- Formát: Hardback
- Rozmer: 241 x 160 mm
- Jazyk: Anglický jazyk
- ISBN: 9783031382291
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