Compact and fast machine learning accelerator for IoT devices [electronic resource] / by Hantao Huang, Hao Yu.
- 作者： Huang, Hantao.
- 其他作者：
- 其他題名：
- Computer architecture and design methodologies.
- 出版： Singapore : Springer Singapore :Imprint: Springer 2019.
- 叢書名： Computer architecture and design methodologies,
- 主題： Machine learning. , Internet of things. , Computational Intelligence. , Processor Architectures. , Optimization.
- ISBN： 9789811333231 (electronic bk.) 、 9789811333224 (paper)
- URL：
點擊此處查看電子書
電子書(校內)
- 一般註：Computing on Edge Devices in Internet-of-things (IoT) -- The Rise of Machine Learning in IoT system -- Least-squares-solver for Shadow Neural Network -- Tensor-solver for Deep Neural Network -- Distributed-solver for Networked Neural Network -- Conclusion. E1084學校採購電子書
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讀者標籤：
- 系統號： 000274342 | 機讀編目格式
館藏資訊
This book presents the latest techniques for machine learning based data analytics on IoT edge devices. A comprehensive literature review on neural network compression and machine learning accelerator is presented from both algorithm level optimization and hardware architecture optimization. Coverage focuses on shallow and deep neural network with real applications on smart buildings. The authors also discuss hardware architecture design with coverage focusing on both CMOS based computing systems and the new emerging Resistive Random-Access Memory (RRAM) based systems. Detailed case studies such as indoor positioning, energy management and intrusion detection are also presented for smart buildings.
摘要註
This book presents the latest techniques for machine learning based data analytics on IoT edge devices. A comprehensive literature review on neural network compression and machine learning accelerator is presented from both algorithm level optimization and hardware architecture optimization. Coverage focuses on shallow and deep neural network with real applications on smart buildings. The authors also discuss hardware architecture design with coverage focusing on both CMOS based computing systems and the new emerging Resistive Random-Access Memory (RRAM) based systems. Detailed case studies such as indoor positioning, energy management and intrusion detection are also presented for smart buildings.