{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T23:34:45Z","timestamp":1762299285068,"version":"3.37.3"},"reference-count":58,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"12","license":[{"start":{"date-parts":[[2020,12,1]],"date-time":"2020-12-01T00:00:00Z","timestamp":1606780800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,12,1]],"date-time":"2020-12-01T00:00:00Z","timestamp":1606780800000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,12,1]],"date-time":"2020-12-01T00:00:00Z","timestamp":1606780800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,12,1]],"date-time":"2020-12-01T00:00:00Z","timestamp":1606780800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["CCF-1919167","CNS-1822099"],"award-info":[{"award-number":["CCF-1919167","CNS-1822099"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst."],"published-print":{"date-parts":[[2020,12]]},"DOI":"10.1109\/tcad.2020.2983370","type":"journal-article","created":{"date-parts":[[2020,3,26]],"date-time":"2020-03-26T19:52:02Z","timestamp":1585252322000},"page":"4748-4759","source":"Crossref","is-referenced-by-count":3,"title":["Binarizing Weights Wisely for Edge Intelligence: Guide for Partial Binarization of Deconvolution-Based Generators"],"prefix":"10.1109","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5699-0870","authenticated-orcid":false,"given":"Jinglan","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaxin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3613-5647","authenticated-orcid":false,"given":"Yukun","family":"Ding","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1046-6379","authenticated-orcid":false,"given":"Xiaowei","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3009-519X","authenticated-orcid":false,"given":"Meng","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiyu","family":"Shi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"journal-title":"Flexible Network Binarization with Layer-wise Priority","year":"2017","author":"zhuang","key":"ref39"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2018.2821561"},{"key":"ref33","first-page":"3123","article-title":"BinaryConnect: Training deep neural networks with binary weights during propagations","author":"courbariaux","year":"2015","journal-title":"Proc Int Conf Adv Neural Inf Process Syst"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.574"},{"journal-title":"Incremental network quantization Towards lossless cnns with low-precision weights","year":"2017","author":"zhou","key":"ref31"},{"journal-title":"Trained ternary quantization","year":"2016","author":"zhu","key":"ref30"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ICME.2018.8486500"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/FPL.2018.00035"},{"journal-title":"On the universal approximability and complexity bounds of quantized ReLU neural networks","year":"2018","author":"ding","key":"ref35"},{"journal-title":"Binarized neural networks Training deep neural networks with weights and activations constrained to +1 or ?1","year":"2016","author":"courbariaux","key":"ref34"},{"journal-title":"Convolutional neural networks using logarithmic data representation","year":"2016","author":"miyashita","key":"ref28"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/MICRO.2016.7783722"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00866"},{"journal-title":"Connecting generative adversarial networks and actor-critic methods","year":"2016","author":"pfau","key":"ref2"},{"key":"ref1","first-page":"64","article-title":"Unsupervised learning for physical interaction through video prediction","author":"finn","year":"2016","journal-title":"Proc Int Conf Adv Neural Inf Process Syst"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_32"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1145\/3079856.3080246"},{"key":"ref21","first-page":"1135","article-title":"Learning both weights and connections for efficient neural network","author":"han","year":"2015","journal-title":"Proc Int Conf Adv Neural Inf Process Syst"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2010.5539957"},{"journal-title":"DoReFa-Net Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients","year":"2016","author":"zhou","key":"ref23"},{"journal-title":"Network in Network","year":"2013","author":"lin","key":"ref26"},{"journal-title":"Speeding up convolutional neural networks with low rank expansions","year":"2014","author":"jaderberg","key":"ref25"},{"journal-title":"Implementation as Matrix Multiplication","year":"2017","key":"ref50"},{"journal-title":"SqueezeNet AlexNet-level accuracy with 50x fewer parameters and","year":"2016","author":"iandola","key":"ref51"},{"journal-title":"Conditional image synthesis with auxiliary classifier gans","year":"2016","author":"odena","key":"ref58"},{"journal-title":"On the quantitative analysis of decoder-based generative models","year":"2016","author":"wu","key":"ref57"},{"key":"ref56","first-page":"1","article-title":"A note on the evaluation of generative models","author":"theis","year":"2016","journal-title":"Proc Int Conf Learn Represent (ICLR)"},{"journal-title":"TF Gans-Comparision","year":"2017","author":"cha","key":"ref55"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.425"},{"journal-title":"DCGAN-tensorflow","year":"2017","author":"kim","key":"ref53"},{"journal-title":"Tensorflow Large-scale machine learning on heterogeneous distributed systems","year":"2016","author":"abadi","key":"ref52"},{"journal-title":"Deep compression Compressing deep neural networks with pruning trained quantization and huffman coding","year":"2015","author":"han","key":"ref10"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/MICRO.2016.7783723"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1145\/3264817"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2017.2705069"},{"key":"ref13","first-page":"513","article-title":"DLAU: A scalable deep learning accelerator unit on FPGA","volume":"36","author":"wang","year":"2017","journal-title":"IEEE Trans Comput -Aided Design Integr Circuits Syst"},{"journal-title":"Quantized neural networks Training neural networks with low precision weights and activations","year":"2016","author":"hubara","key":"ref14"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CoolChips.2018.8373076"},{"journal-title":"Ternary Weight Networks","year":"2016","author":"li","key":"ref16"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01237-3_23"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2018.2857019"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/ISVLSI.2018.00034"},{"journal-title":"Nips 2016 tutorial Generative adversarial networks","year":"2016","author":"goodfellow","key":"ref4"},{"key":"ref3","first-page":"2234","article-title":"Improved techniques for training GANs","author":"salimans","year":"2016","journal-title":"Proc Int Conf Adv Neural Inf Process Syst"},{"journal-title":"Unsupervised representation learning with deep convolutional generative adversarial networks","year":"2015","author":"radford","key":"ref6"},{"journal-title":"Photo-realistic single image super-resolution using a generative adversarial network","year":"2016","author":"ledig","key":"ref5"},{"journal-title":"Apple Inc","year":"2017","key":"ref8"},{"journal-title":"Progressive growing of GANs for improved quality stability and variation","year":"2017","author":"karras","key":"ref7"},{"journal-title":"Least squares generative adversarial networks","year":"2016","author":"mao","key":"ref49"},{"key":"ref9","first-page":"2285","article-title":"Compressing neural networks with the hashing trick","author":"chen","year":"2015","journal-title":"Proc Int Conf Mach Learn"},{"journal-title":"Generative adversarial text to image synthesis","year":"2016","author":"reed","key":"ref46"},{"key":"ref45","first-page":"2672","article-title":"Generative adversarial nets","author":"goodfellow","year":"2014","journal-title":"Proc Int Conf Adv Neural Inf Process Syst"},{"key":"ref48","first-page":"214","article-title":"Wasserstein generative adversarial networks","author":"arjovsky","year":"2017","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref47","first-page":"2180","article-title":"InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets","author":"chen","year":"2016","journal-title":"Proc 30th Int Conf Adv Neural Inf Process Syst"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2018.00095"},{"journal-title":"Efficient hybrid network architectures for extremely quantized neural networks enabling intelligence at the edge","year":"2019","author":"chakraborty","key":"ref41"},{"journal-title":"Binary generative adversarial networks for image retrieval","year":"2017","author":"song","key":"ref44"},{"journal-title":"PCA-driven hybrid network design for enabling intelligence at the edge","year":"2019","author":"chakraborty","key":"ref43"}],"container-title":["IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems"],"original-title":[],"link":[{"URL":"https:\/\/ieeexplore.ieee.org\/ielam\/43\/9265421\/9047891-aam.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/43\/9265421\/09047891.pdf?arnumber=9047891","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,27]],"date-time":"2022-04-27T14:06:26Z","timestamp":1651068386000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9047891\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12]]},"references-count":58,"journal-issue":{"issue":"12"},"URL":"https:\/\/doi.org\/10.1109\/tcad.2020.2983370","relation":{},"ISSN":["0278-0070","1937-4151"],"issn-type":[{"type":"print","value":"0278-0070"},{"type":"electronic","value":"1937-4151"}],"subject":[],"published":{"date-parts":[[2020,12]]}}}