A Low Power In-DRAM Architecture for Quantized CNNs using Fast Winograd Convolutions-Reference-Cited by-同舟云学术

A Low Power In-DRAM Architecture for Quantized CNNs using Fast Winograd Convolutions

Published:2020-09-28 Issue: Volume: Page:
ISSN:
Container-title:The International Symposium on Memory Systems
language:
Short-container-title:

Author:

Ghaffar Muhammad Mohsin¹,Sudarshan Chirag¹,Weis Christian¹,Jung Matthias²,Wehn Norbert¹

Affiliation:

1. TU Kaiserslautern, Germany

2. Fraunhofer IESE, Germany

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3422575.3422790

Reference67 articles.

1. A. Agrawal A. Jaiswal D. Roy B. Han G. Srinivasan A. Ankit and K. Roy. 2019. Xcel-RAM: Accelerating Binary Neural Networks in High-Throughput SRAM Compute Arrays. IEEE Transactions on Circuits and Systems I: Regular Papers PP (04 2019) 1–13. https://doi.org/10.1109/TCSI.2019.2907488 A. Agrawal A. Jaiswal D. Roy B. Han G. Srinivasan A. Ankit and K. Roy. 2019. Xcel-RAM: Accelerating Binary Neural Networks in High-Throughput SRAM Compute Arrays. IEEE Transactions on Circuits and Systems I: Regular Papers PP (04 2019) 1–13. https://doi.org/10.1109/TCSI.2019.2907488

2. S. Angizi and D. Fan. 2019. Accelerating Bulk Bit-Wise X(N)OR Operation in Processing-in-DRAM Platform. CoRR abs/1904.05782(2019). arxiv:1904.05782http://arxiv.org/abs/1904.05782 S. Angizi and D. Fan. 2019. Accelerating Bulk Bit-Wise X(N)OR Operation in Processing-in-DRAM Platform. CoRR abs/1904.05782(2019). arxiv:1904.05782http://arxiv.org/abs/1904.05782

3. M. Blott T. B. Preußer N. J. Fraser G. Gambardella K. O’Brien and Y. Umuroglu. 2018. FINN-R: An End-to-End Deep-Learning Framework for Fast Exploration of Quantized Neural Networks. CoRR abs/1809.04570(2018). arxiv:1809.04570http://arxiv.org/abs/1809.04570 M. Blott T. B. Preußer N. J. Fraser G. Gambardella K. O’Brien and Y. Umuroglu. 2018. FINN-R: An End-to-End Deep-Learning Framework for Fast Exploration of Quantized Neural Networks. CoRR abs/1809.04570(2018). arxiv:1809.04570http://arxiv.org/abs/1809.04570

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