Can FPGAs Beat GPUs in Accelerating Next-Generation Deep Neural Networks?-Reference-Cited by-同舟云学术

Can FPGAs Beat GPUs in Accelerating Next-Generation Deep Neural Networks?

Published:2017-02-22 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2017 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays
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Author:

Nurvitadhi Eriko¹,Venkatesh Ganesh¹,Sim Jaewoong¹,Marr Debbie¹,Huang Randy²,Ong Gee Hock Jason³,Liew Yeong Tat³,Srivatsan Krishnan¹,Moss Duncan¹,Subhaschandra Suchit¹,Boudoukh Guy⁴

Affiliation:

1. Intel, Hillsboro, OR, USA

2. Intel, San Jose, USA

3. Intel, Penang, Malaysia

4. Intel, Haifa, Israel

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3020078.3021740

Reference30 articles.

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2. M. Rastegari V. Ordonez J. Redmon A. Farhadi "XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks " arXiv:1603.05279 [cs.CV] M. Rastegari V. Ordonez J. Redmon A. Farhadi "XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks " arXiv:1603.05279 [cs.CV]

3. F. Li B. Liu. "Ternary Weight Networks " arXiv:1605.04711 [cs.CV] F. Li B. Liu. "Ternary Weight Networks " arXiv:1605.04711 [cs.CV]

4. G. Venkatesh E. Nurvitadhi D. Marr ".Accelerating Deep Convolutional Networks Using Low-Precision and Sparsity " ICASSP 2017. G. Venkatesh E. Nurvitadhi D. Marr ".Accelerating Deep Convolutional Networks Using Low-Precision and Sparsity " ICASSP 2017.

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