{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T14:40:25Z","timestamp":1768401625225,"version":"3.49.0"},"reference-count":50,"publisher":"IEEE","license":[{"start":{"date-parts":[[2020,7,1]],"date-time":"2020-07-01T00:00:00Z","timestamp":1593561600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,7,1]],"date-time":"2020-07-01T00:00:00Z","timestamp":1593561600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,7,1]],"date-time":"2020-07-01T00:00:00Z","timestamp":1593561600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,7]]},"DOI":"10.1109\/ijcnn48605.2020.9206872","type":"proceedings-article","created":{"date-parts":[[2020,9,30]],"date-time":"2020-09-30T00:40:33Z","timestamp":1601426433000},"page":"1-9","source":"Crossref","is-referenced-by-count":5,"title":["Hyperparameter Optimization in Binary Communication Networks for Neuromorphic Deployment"],"prefix":"10.1109","author":[{"given":"Maryam","family":"Parsa","sequence":"first","affiliation":[]},{"given":"Catherine D.","family":"Schuman","sequence":"additional","affiliation":[]},{"given":"Prasanna","family":"Date","sequence":"additional","affiliation":[]},{"given":"Derek C.","family":"Rose","sequence":"additional","affiliation":[]},{"given":"Bill","family":"Kay","sequence":"additional","affiliation":[]},{"given":"J. Parker","family":"Mitchell","sequence":"additional","affiliation":[]},{"given":"Steven R.","family":"Young","sequence":"additional","affiliation":[]},{"given":"Ryan","family":"Dellana","sequence":"additional","affiliation":[]},{"given":"William","family":"Severa","sequence":"additional","affiliation":[]},{"given":"Thomas E.","family":"Potok","sequence":"additional","affiliation":[]},{"given":"Kaushik","family":"Roy","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-30487-4_28"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2019.00095"},{"key":"ref33","article-title":"Training spiking deep networks for neuromorphic hardware","author":"hunsberger","year":"2016"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2017.00682"},{"key":"ref31","first-page":"1412","article-title":"Slayer: Spike layer error reassignment in time","author":"shrestha","year":"2018","journal-title":"Advances in neural information processing systems"},{"key":"ref30","article-title":"Learning multiple layers of features from tiny images","author":"krizhevsky","year":"2009"},{"key":"ref37","first-page":"7005","article-title":"Hybrid macro\/micro level backpropagation for training deep spiking neural networks","author":"jin","year":"2018","journal-title":"Advances in neural information processing systems"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1145\/3354265.3354275"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2019.09.007"},{"key":"ref34","article-title":"Enabling spike-based backpropagation in state-of-the-art deep neural network architectures","author":"lee","year":"2019"},{"key":"ref28","article-title":"Mnist handwritten digit database","volume":"2","author":"lecun","year":"2010","journal-title":"ATT Labs"},{"key":"ref27","year":"0","journal-title":"Scikit-Optimize python package"},{"key":"ref29","article-title":"Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms","author":"xiao","year":"2017"},{"key":"ref2","article-title":"A survey of neuromorphic computing and neural networks in hardware","author":"schuman","year":"2017"},{"key":"ref1","article-title":"Non-neural network applications for spiking neuromorphic hardware","author":"aimone","year":"2018","journal-title":"3rd International Workshop on Post-Moore’s Era Supercomputing (PMES 2018) Dallas TX"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1007\/s11063-017-9733-0"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2019.8852139"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/3205455.3205489"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2016.7727192"},{"key":"ref23","article-title":"Differential evolution and bayesian optimisation for hyper-parameter selection in mixed-signal neuromorphic circuits applied to uav obstacle avoidance","author":"salt","year":"2017"},{"key":"ref26","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1109\/JPROC.2015.2494218","article-title":"Taking the human out of the loop: A review of bayesian optimization","volume":"104","author":"shahriari","year":"2015","journal-title":"Proceedings of the IEEE"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-92537-0_6"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/BigData47090.2019.9006239"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2016.07.416"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-35289-8_26"},{"key":"ref40","article-title":"A tandem learning rule for efficient and rapid inference on deep spiking neural networks","author":"wu","year":"0"},{"key":"ref12","first-page":"2546","article-title":"Algorithms for hyper-parameter optimization","author":"bergstra","year":"2011","journal-title":"Advances in neural information processing systems"},{"key":"ref13","article-title":"Preliminary evaluation of hyperopt algorithms on hpolib","author":"bergstra","year":"2014","journal-title":"ICML AutoML Workshop"},{"key":"ref14","first-page":"2951","article-title":"Practical bayesian optimization of machine learning algorithms","author":"snoek","year":"2012","journal-title":"Advances in neural information processing systems"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298621"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/HiPC.2018.00014"},{"key":"ref17","doi-asserted-by":"crossref","DOI":"10.1609\/aaai.v31i1.10647","article-title":"Efficient hyperparameter optimization for deep learning algorithms using deterministic rbf surrogates","author":"ilievski","year":"2017","journal-title":"Thirty-First AAAI Conference on Artificial Intelligence"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-12-815480-9.00015-3"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/3146347.3146355"},{"key":"ref4","first-page":"5549","article-title":"A general framework for constrained bayesian optimization using information-based search","volume":"17","author":"hern\u00e1ndez-lobato","year":"2016","journal-title":"The Journal of Machine Learning Research"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.25080\/Majora-8b375195-003"},{"key":"ref6","first-page":"281","article-title":"Random search for hyper-parameter opti-mization","volume":"13","author":"bergstra","year":"2012","journal-title":"Journal of Machine Learning Research"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ICCAD45719.2019.8942046"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/BigData47090.2019.9006383"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-018-0015-y"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/MM.2018.112130359"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2020.00667"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33011311"},{"key":"ref45","article-title":"Spikegrad: An ann-equivalent computation model for implementing backpropagation with spikes","author":"thiele","year":"2019"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2014.2304638"},{"key":"ref47","first-page":"1","article-title":"Low-power deep learning inference using the spinnaker neu-romorphic platform","author":"vineyard","year":"2019","journal-title":"Proceedings of the 7th Annual Neuro-inspired Computational Elements Workshop"},{"key":"ref42","first-page":"173","article-title":"Homeostasis-based cnn-to-snn conversion of inception and residual architectures","author":"xing","year":"2019","journal-title":"International Conference on Neural Information Processing"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/ICCC.2019.00020"},{"key":"ref44","article-title":"Spike-based causal inference for weight alignment","author":"guerguiev","year":"2019"},{"key":"ref43","article-title":"Spiking deep residual network","author":"hu","year":"2018"}],"event":{"name":"2020 International Joint Conference on Neural Networks (IJCNN)","location":"Glasgow, United Kingdom","start":{"date-parts":[[2020,7,19]]},"end":{"date-parts":[[2020,7,24]]}},"container-title":["2020 International Joint Conference on Neural Networks (IJCNN)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9200848\/9206590\/09206872.pdf?arnumber=9206872","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,21]],"date-time":"2022-11-21T06:21:20Z","timestamp":1669011680000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9206872\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7]]},"references-count":50,"URL":"https:\/\/doi.org\/10.1109\/ijcnn48605.2020.9206872","relation":{},"subject":[],"published":{"date-parts":[[2020,7]]}}}