| 书目名称 | Towards Heterogeneous Multi-core Systems-on-Chip for Edge Machine Learning |
| 副标题 | Journey from Single- |
| 编辑 | Vikram Jain,Marian Verhelst |
| 视频video | http://file.papertrans.cn/927/926945/926945.mp4 |
| 概述 | 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 |
| 图书封面 |  |
| 描述 | .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.. |
| 出版日期 | Book 2024 |
| 关键词 | Edge AI; machine learning; hardware accelerators; homogeneous and heterogeneous systems; deep learning |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-031-38230-7 |
| isbn_softcover | 978-3-031-38232-1 |
| isbn_ebook | 978-3-031-38230-7 |
| copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
|Archiver|手机版|小黑屋|
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