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. 2015 Jul 9;9:222. doi: 10.3389/fnins.2015.00222

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Copyright © 2015 Stromatias, Neil, Pfeiffer, Galluppi, Furber and Liu.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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Impact of different rounding methods during training on network performance with reduced weight bit precision. (A) Effectiveness of the dual-copy rounding weight training paradigm. Training at full precision and later rounding performs consistently worse than the dual-copy rounding method introduced in this paper. Rounding the weights during training can prevent learning entirely at low-precision regimes. The results show averages of five independent runs with different random seeds. (B) Increase in classification accuracy of a spiking DBN with Q3.1 precision weights due to the dual-copy rounding method for input rates of 100 and 1500 Hz. Results over four trials.