{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T22:25:40Z","timestamp":1730240740223,"version":"3.28.0"},"reference-count":14,"publisher":"IEEE","license":[{"start":{"date-parts":[[2023,12,4]],"date-time":"2023-12-04T00:00:00Z","timestamp":1701648000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,12,4]],"date-time":"2023-12-04T00:00:00Z","timestamp":1701648000000},"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":[[2023,12,4]]},"DOI":"10.1109\/icecs58634.2023.10382772","type":"proceedings-article","created":{"date-parts":[[2024,1,10]],"date-time":"2024-01-10T19:38:25Z","timestamp":1704915505000},"page":"1-4","source":"Crossref","is-referenced-by-count":0,"title":["An Empirical Study of Convolutional Neural Network Compressions within Low-Power Devices"],"prefix":"10.1109","author":[{"given":"Thomas","family":"Garbay","sequence":"first","affiliation":[{"name":"Sorbonne Université CNRS, LIP6,Paris,France,F-75005"}]},{"given":"Karim","family":"Hocine","sequence":"additional","affiliation":[{"name":"Sorbonne Université CNRS, LIP6,Paris,France,F-75005"}]},{"given":"Khalil","family":"Hachicha","sequence":"additional","affiliation":[{"name":"Sorbonne Université CNRS, LIP6,Paris,France,F-75005"}]},{"given":"Andrea","family":"Pinna","sequence":"additional","affiliation":[{"name":"Sorbonne Université CNRS, LIP6,Paris,France,F-75005"}]},{"given":"Bertrand","family":"Granado","sequence":"additional","affiliation":[{"name":"Sorbonne Université CNRS, LIP6,Paris,France,F-75005"}]}],"member":"263","reference":[{"author":"David","first-page":"12","article-title":"TensorFlow Lite Micro: Embedded Machine Learning on TinyML Systems","key":"ref1"},{"key":"ref2","article-title":"Benchmarking TinyML Systems: Challenges and Direction","author":"Banbury","year":"2021","journal-title":"arXiv:2003.04821 [cs]"},{"doi-asserted-by":"publisher","key":"ref3","DOI":"10.1109\/CVPR.2018.00286"},{"key":"ref4","article-title":"An Empirical Study of Low Precision Quantization for TinyML","author":"Zhuo","year":"2022","journal-title":"arXiv:2203.05492 [cs]"},{"key":"ref5","first-page":"3123","article-title":"BinaryConnect: Training Deep Neural Networks with binary weights during propagations","volume":"28","author":"Courbariaux","year":"2015","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref6","article-title":"Power-of-Two Quantization for Low Bitwidth and Hardware Compliant Neural Networks","author":"Przewlocka-Rus","year":"2022","journal-title":"arXiv:2203.05025 arXiv:2203.05025 [cs]"},{"key":"ref7","article-title":"Learning both Weights and Connections for Efficient Neural Network","volume":"28","author":"Han","year":"2015","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref8","article-title":"Dynamic Network Surgery for Efficient DNNs","volume":"29","author":"Guo","year":"2016","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref9","article-title":"Pruning Filters for Efficient ConvNets","author":"Li","year":"2017","journal-title":"arXiv:1608.08710 [cs]"},{"key":"ref10","article-title":"Rethinking the Smaller-Norm-Less-Informative Assumption in Channel Pruning of Convolution Layers","author":"Ye","year":"2018","journal-title":"arXiv:1802.00124 [cs]"},{"doi-asserted-by":"publisher","key":"ref11","DOI":"10.1109\/CVPR.2019.00447"},{"doi-asserted-by":"publisher","key":"ref12","DOI":"10.1109\/5.726791"},{"key":"ref13","article-title":"Deep Residual Learning for Image Recognition","author":"He","year":"2015","journal-title":"arXiv:1512.03385 [cs]"},{"doi-asserted-by":"publisher","key":"ref14","DOI":"10.1109\/SOCC56010.2022.9908108"}],"event":{"name":"2023 30th IEEE International Conference on Electronics, Circuits and Systems (ICECS)","start":{"date-parts":[[2023,12,4]]},"location":"Istanbul, Turkiye","end":{"date-parts":[[2023,12,7]]}},"container-title":["2023 30th IEEE International Conference on Electronics, Circuits and Systems (ICECS)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10382705\/10382711\/10382772.pdf?arnumber=10382772","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,13]],"date-time":"2024-01-13T18:24:58Z","timestamp":1705170298000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10382772\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,4]]},"references-count":14,"URL":"https:\/\/doi.org\/10.1109\/icecs58634.2023.10382772","relation":{},"subject":[],"published":{"date-parts":[[2023,12,4]]}}}