Energy-efficient computing-in-memory architecture for AI processor: device, circuit, architecture perspective-Reference-Cited by-同舟云学术

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Energy-efficient computing-in-memory architecture for AI processor: device, circuit, architecture perspective

Published:2021-05-11 Issue:6 Volume:64 Page:
ISSN:1674-733X
Container-title:Science China Information Sciences
language:en
Short-container-title:Sci. China Inf. Sci.

Author:

Chang Liang,Li Chenglong,Zhang Zhaomin,Xiao Jianbiao,Liu Qingsong,Zhu Zhen,Li Weihang,Zhu Zixuan,Yang Siqi,Zhou Jun

Publisher

Springer Science and Business Media LLC

Subject

General Computer Science

Link

https://link.springer.com/content/pdf/10.1007/s11432-021-3234-0.pdf

Reference95 articles.

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2. Wess M, Manoj P D S, Jantsch A. Neural network based ECG anomaly detection on FPGA and trade-off analysis. In: Proceedings of IEEE International Symposium on Circuits and Systems (ISCAS), 2017. 1–4

3. Zairi H, Talha M K, Meddah K, et al. FPGA-based system for artificial neural network arrhythmia classification. Neural Comput Appl, 2019, 32: 4105–4120

4. Chen Y, Luo T, Liu S, et al. Dadiannao: a machine-learning supercomputer. In: Proceedings of the 47th Annual IEEE/ACM International Symposium on Microarchitecture, 2014. 609–622

5. Du Z, Fasthuber R, Chen T, et al. Shidiannao: shifting vision processing closer to the sensor. In: Proceedings of ACM/IEEE 42nd Annual International Symposium on Computer Architecture (ISCA), 2015. 92–104

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