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. 2018 Jun 13;9:2331. doi: 10.1038/s41467-018-04482-4

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© The Author(s) 2018

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 5 — Ex-situ training experimental results. a, b The normalized difference between the target and the actual conductances after tuning in a the first and b the second layer of the network for the hardware-oblivious training approach; c Time response of the trained network for 6 different input patterns, in particular showing less than 5 μs propagation delay. Perceptron output voltage for d, f hardware-oblivious and e, g hardware-aware ex-situ training approaches, with d-g panels showing measured results for training/test patterns