Learning automata based energy-efficient AI hardware design for IoT applications
Author:
Affiliation:
1. Microsystems Group, School of Engineering Newcastle University, Newcastle upon Tyne NE1 7RU, UK
2. CAIR, University of Agder, Postboks 422 4604 Kristiansand, Norway
Abstract
Funder
Engineering and Physical Sciences Research Council
Publisher
The Royal Society
Subject
General Physics and Astronomy,General Engineering,General Mathematics
Link
https://royalsocietypublishing.org/doi/pdf/10.1098/rsta.2019.0593
Reference31 articles.
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2. Biswas A Chandrakasan AP. 2018 Conv-RAM: an energy-efficient SRAM with embedded convolution computation for low-power CNN-based machine learning applications. In Proc. 2018 IEEE Int. Solid-State Circuits Conf. (ISSCC) San Francisco CA 11–18 February 2018 pp. 488–490. New York NY: IEEE.
3. Chen M Miao Y Jian X Wang X Humar I. 2018 Cognitive-LPWAN: towards intelligent wireless services in hybrid low power wide area networks. ()
4. Data and Power Efficient Intelligence with Neuromorphic Learning Machines
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