{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T05:26:44Z","timestamp":1730266004881,"version":"3.28.0"},"reference-count":40,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,7,18]],"date-time":"2021-07-18T00:00:00Z","timestamp":1626566400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,7,18]],"date-time":"2021-07-18T00:00:00Z","timestamp":1626566400000},"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":[[2021,7,18]]},"DOI":"10.1109\/ijcnn52387.2021.9533393","type":"proceedings-article","created":{"date-parts":[[2021,9,20]],"date-time":"2021-09-20T21:27:41Z","timestamp":1632173261000},"page":"1-8","source":"Crossref","is-referenced-by-count":3,"title":["Synchronous Weight Quantization-Compression for Low-Bit Quantized Neural Network"],"prefix":"10.1109","author":[{"given":"Yuzhong","family":"Jiao","sequence":"first","affiliation":[{"name":"United Microelectronics Centre,Hong Kong,China"}]},{"given":"Sha","family":"Li","sequence":"additional","affiliation":[{"name":"United Microelectronics Centre,Hong Kong,China"}]},{"given":"Xiao","family":"Huo","sequence":"additional","affiliation":[{"name":"United Microelectronics Centre,Hong Kong,China"}]},{"given":"Yiu Kei","family":"Li","sequence":"additional","affiliation":[{"name":"United Microelectronics Centre,Hong Kong,China"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref38","article-title":"Utilizing Explainable AI for Quantization and Pruning of Deep Neural Networks","author":"sabih","year":"2020","journal-title":"ArXiv Preprint"},{"key":"ref33","article-title":"Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients","author":"zhou","year":"2016","journal-title":"ArXiv Preprint"},{"key":"ref32","article-title":"Incremental network quantization: Towards lossless cnns with low-precision weights","author":"zhou","year":"2017","journal-title":"ArXiv Preprint"},{"key":"ref31","first-page":"525","article-title":"XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks","author":"rastegari","year":"0","journal-title":"Proc of ECCV 2016"},{"key":"ref30","article-title":"Binarized Neural Networks","author":"hubara","year":"2016","journal-title":"Advances in neural information processing systems"},{"key":"ref37","first-page":"2559","volume":"9","author":"seo","year":"2019","journal-title":"Efficient weights quantization of convolutional neural networks using kernel density estimation based non-uniform quantizer"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/VLSICircuits18222.2020.9162811"},{"key":"ref35","first-page":"454","article-title":"Coreset-Based Neural Network Compression","author":"dubey","year":"2018","journal-title":"ECCV"},{"key":"ref34","first-page":"4324","article-title":"Weightless: Lossy weight encoding for deep neural network compression","author":"reagen","year":"2018","journal-title":"ICML"},{"key":"ref10","first-page":"1135","article-title":"Learning both weights and connections for efficient neural network","author":"han","year":"2015","journal-title":"Advances in neural information processing systems 2015"},{"key":"ref40","article-title":"Learning multiple layers of features from tiny images","author":"krizhevsky","year":"2009","journal-title":"Tech Report"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2017.241"},{"key":"ref12","article-title":"Compressing Neural Networks with the Hashing Trick","author":"chen","year":"2015","journal-title":"ICML"},{"key":"ref13","first-page":"4857","article-title":"Learning to prune deep neural networks via layer-wise optimal brain surgeon","author":"dong","year":"2017","journal-title":"Advances in Neural Information Processing Systems 2017"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1117\/12.20700"},{"key":"ref15","article-title":"Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization, and Huffman Coding","author":"han","year":"0","journal-title":"Proc of ICLR"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ISCAS.2018.8351234"},{"key":"ref17","article-title":"Double Viterbi: Weight encoding for high compression ratio and fast on-chip reconstruction for deep neural network","author":"ahn","year":"2019","journal-title":"ICLRE"},{"key":"ref18","article-title":"Improving neural network quantization without retraining using outlier channel splitting","author":"zhao","year":"2019","journal-title":"ICML"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2017.2761740"},{"key":"ref28","article-title":"Ternary weight networks","author":"li","year":"2016","journal-title":"ArXiv Preprint"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.2352\/ISSN.2470-1173.2020.2.SDA-098"},{"key":"ref27","first-page":"2849","article-title":"Fixed point quantization of deep convolutional networks","author":"lin","year":"2016","journal-title":"ICML"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/SII46433.2020.9026246"},{"key":"ref6","article-title":"QuartzNet: Deep automatic speech recognition with 1D time-channel separable convolutions","author":"kriman","year":"2019","journal-title":"ArXiv Preprint"},{"key":"ref29","article-title":"BinaryConnect: Training Deep Neural Networks with binary weights during propagations","author":"courbariaux","year":"2015","journal-title":"Advances in neural information processing systems"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2013.6639345"},{"key":"ref8","article-title":"Deep learning based multi-source localization with source splitting and its effectiveness in multi-talker speech recognition","author":"subramanian","year":"2021","journal-title":"ArXiv Preprint"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1002\/admt.202000262"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-019-0458-z"},{"key":"ref9","article-title":"Exploiting Weight Redundancy in CNNs: Beyond Pruning and Quantization","author":"wen","year":"2020","journal-title":"ArXiv Preprint"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1155\/2018\/7068349"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1631\/FITEE.1700789"},{"key":"ref22","article-title":"Quantized neural networks: Training neural networks with low precision weights and activations","author":"hubara","year":"2016","journal-title":"ArXiv Preprint"},{"key":"ref21","article-title":"Quantizing deep convolutional networks for efficient inference: A whitepaper","author":"krishnamoorthi","year":"2018","journal-title":"ArXiv Preprint"},{"key":"ref24","article-title":"Weight Compression-Friendly Binarized Neural Network","author":"jiao","year":"2020","journal-title":"IEEE GCAIOT"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00826"},{"key":"ref26","article-title":"Convolutional neural networks using logarithmic data representation","author":"miyashita","year":"2016","journal-title":"ArXiv Preprint"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.29007\/25x3"}],"event":{"name":"2021 International Joint Conference on Neural Networks (IJCNN)","start":{"date-parts":[[2021,7,18]]},"location":"Shenzhen, China","end":{"date-parts":[[2021,7,22]]}},"container-title":["2021 International Joint Conference on Neural Networks (IJCNN)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9533266\/9533267\/09533393.pdf?arnumber=9533393","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,2]],"date-time":"2022-08-02T23:33:07Z","timestamp":1659483187000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9533393\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,18]]},"references-count":40,"URL":"https:\/\/doi.org\/10.1109\/ijcnn52387.2021.9533393","relation":{},"subject":[],"published":{"date-parts":[[2021,7,18]]}}}