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. 2018 Aug 29;12:583. doi: 10.3389/fnins.2018.00583

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Copyright © 2018 Detorakis, Sheik, Augustine, Paul, Pedroni, Dutt, Krichmar, Cauwenberghs and Neftci.

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) and the copyright owner(s) 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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Training an MNIST network with event-driven Random Back-propagation compared to GPU simulations. (A) Network architecture. (B) MNIST Classification error on the test set using a fully connected 784-100-10 network on NSAT (8 bit fixed-point weights, 16 bits state components) and on GPU (TensorFlow, floating-point 32 bits). (C) Energy efficiency of learning in the NSAT (lower left is best). The number of operations necessary to reach a given accuracy is lower or equal in the spiking neural network (NSAT-SynOps) compared to the artificial neural network (GPU-MACs) for classification errors at or above 4%. (D) Histogram of synaptic weights of the NSAT network after training. One epoch equals a full presentation of the training set.