Towards Heterogeneous Multi-core Systems-on-Chip for Edge Machine Learning
Journey from Single-core Acceleration to Multi-core Heterogeneous Systems
Buch in deiner Nähe kaufen
Beschreibung
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.
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, several architectural 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.
Autor*in
Vikram Jain
Themen in »Towards Heterogeneous Multi-core Systems-on-Chip for Edge Machine Learning«
Edge AI machine learning hardware accelerators homogeneous and heterogeneous systems deep learning