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The inference of the neural network can be greatly accelerated by the vector\u2010matrix multiplication (VMM) performed within a crossbar array of memristive devices in one step. Nevertheless, the implementation of the VMM needs complex peripheral circuits, and the complexity further increases as non\u2010idealities of memristive devices prevent precise conductance tuning (especially for the online training) and largely degrade the performance of the deep neural networks (DNNs). Herein, an efficient online training method of the memristive deep belief net (DBN) is presented. The proposed memristive DBN uses stochastically binarized activations, reducing the complexity of peripheral circuits, and uses the contrastive divergence (CD)\u2010based gradient descent learning algorithm. The analog VMM and digital CD are performed separately in a mixed\u2010signal hardware arrangement, making the memristive DBN highly immune to non\u2010idealities of synaptic devices. The number of write operations on memristive devices is reduced by two orders of magnitude. The recognition accuracy of 95\u201397% can be achieved for the MNIST dataset using pulsed synaptic behaviors of various memristive synaptic devices.<\/jats:p><\/jats:sec>","DOI":"10.1002\/aisy.202100249","type":"journal-article","created":{"date-parts":[[2022,2,23]],"date-time":"2022-02-23T07:05:40Z","timestamp":1645599940000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Efficient Training of the Memristive Deep Belief Net Immune to Non\u2010Idealities of the Synaptic Devices"],"prefix":"10.1002","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7206-8659","authenticated-orcid":false,"given":"Wei","family":"Wang","sequence":"first","affiliation":[{"name":"The Andrew and Erna Viterbi Faculty of Electrical and Computer Engineering Technion-Israel Institute of Technology  Haifa 3200003 Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Barak","family":"Hoffer","sequence":"additional","affiliation":[{"name":"The Andrew and Erna Viterbi Faculty of Electrical and Computer Engineering Technion-Israel Institute of Technology  Haifa 3200003 Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tzofnat","family":"Greenberg-Toledo","sequence":"additional","affiliation":[{"name":"The Andrew and Erna Viterbi Faculty of Electrical and Computer Engineering Technion-Israel Institute of Technology  Haifa 3200003 Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Li","sequence":"additional","affiliation":[{"name":"The Andrew and Erna Viterbi Faculty of Electrical and Computer Engineering Technion-Israel Institute of Technology  Haifa 3200003 Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minhui","family":"Zou","sequence":"additional","affiliation":[{"name":"The Andrew and Erna Viterbi Faculty of Electrical and Computer Engineering Technion-Israel Institute of Technology  Haifa 3200003 Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eric","family":"Herbelin","sequence":"additional","affiliation":[{"name":"The Andrew and Erna Viterbi Faculty of Electrical and Computer Engineering Technion-Israel Institute of Technology  Haifa 3200003 Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ronny","family":"Ronen","sequence":"additional","affiliation":[{"name":"The Andrew and Erna Viterbi Faculty of Electrical and Computer Engineering Technion-Israel Institute of Technology  Haifa 3200003 Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoxin","family":"Xu","sequence":"additional","affiliation":[{"name":"Institute of Microelectronics Chinese Academy of Sciences  Beijing 100029 P. 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