Using Stochastic Computing to Reduce the Hardware Requirements for a Restricted Boltzmann Machine Classifier-Reference-Cited by-同舟云学术

Using Stochastic Computing to Reduce the Hardware Requirements for a Restricted Boltzmann Machine Classifier

Published:2016-02-21 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2016 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays
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Author:

Li Bingzhe¹,Najafi M. Hassan¹,Lilja David J.¹

Affiliation:

1. University of Minnesota, Twin Cities, Minneapolis, MN, USA

Funder

National Science Foundation

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/2847263.2847340

Reference12 articles.

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3. L.-W. Kim S. Asaad and R. Linsker. A fully pipelined FPGA architecture of a factored restricted boltzmann machine artificial neural network. ACM Transactions on Reconfigurable Technology and Systems (TRETS) 7(1):5 2014. 10.1145/2539125 L.-W. Kim S. Asaad and R. Linsker. A fully pipelined FPGA architecture of a factored restricted boltzmann machine artificial neural network. ACM Transactions on Reconfigurable Technology and Systems (TRETS) 7(1):5 2014. 10.1145/2539125

4. Y. LeCun and C. Cortes. Mnist handwritten digit database. AT&T Labs {Online}. Available: http://yann. lecun. com/exdb/mnist 2010. Y. LeCun and C. Cortes. Mnist handwritten digit database. AT&T Labs {Online}. Available: http://yann. lecun. com/exdb/mnist 2010.

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