Towards Heterogeneous Multi-core Systems-on-Chip for Edge Machine Learning
Produktnummer:
18267996b8e2ea4f139f2c0ba79acc23e5
| Autor: | Jain, Vikram Verhelst, Marian |
|---|---|
| Themengebiete: | Edge AI deep learning hardware accelerators homogeneous and heterogeneous systems machine learning |
| Veröffentlichungsdatum: | 17.09.2023 |
| EAN: | 9783031382291 |
| Sprache: | Englisch |
| Seitenzahl: | 186 |
| Produktart: | Gebunden |
| Verlag: | Springer International Publishing |
| Untertitel: | Journey from Single-core Acceleration to Multi-core Heterogeneous Systems |
Produktinformationen "Towards Heterogeneous Multi-core Systems-on-Chip for Edge 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.
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