Equivalent-accuracy accelerated neural-network training using analogue memory

Author:

Ambrogio Stefano,Narayanan Pritish,Tsai Hsinyu,Shelby Robert M.,Boybat Irem,di Nolfo Carmelo,Sidler Severin,Giordano Massimo,Bodini Martina,Farinha Nathan C. P.,Killeen Benjamin,Cheng Christina,Jaoudi Yassine,Burr Geoffrey W.

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

Reference44 articles.

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3. Gupta, S., Agrawal, A., Gopalakrishnan, K. & Narayanan, P. Deep learning with limited numerical precision. In Proc. 30th International Conference on Machine Learning 1737–1746 (Association for Computing Machinery, 2015).

4. Merolla, P., Appuswamy, R., Arthur, J., Esser, S. K. & Modha, D. Deep neural networks are robust to weight binarization and other non-linear distortions. Preprint at https://arxiv.org/abs/1606.01981 (2016).

5. Nurvitadhi, E. et al. Can FPGAs beat GPUs in accelerating next-generation deep neural networks? In Proc. 2017 ACM/SIGSA International Symposium of Field-Programmable Gate Arrays 5–14 (Association for Computing Machinery, 2017).

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