{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T13:42:26Z","timestamp":1765546946177,"version":"3.28.0"},"reference-count":40,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,8,1]],"date-time":"2022-08-01T00:00:00Z","timestamp":1659312000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,8,1]],"date-time":"2022-08-01T00:00:00Z","timestamp":1659312000000},"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":[[2022,8,1]]},"DOI":"10.1109\/coins54846.2022.9854973","type":"proceedings-article","created":{"date-parts":[[2022,8,19]],"date-time":"2022-08-19T19:38:47Z","timestamp":1660937927000},"page":"1-6","source":"Crossref","is-referenced-by-count":1,"title":["Profiling the real world potential of neural network compression"],"prefix":"10.1109","author":[{"given":"Joe","family":"Lorentz","sequence":"first","affiliation":[{"name":"DataThings S. A.,Luxembourg"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Assaad","family":"Moawad","sequence":"additional","affiliation":[{"name":"DataThings S. A.,Luxembourg"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thomas","family":"Hartmann","sequence":"additional","affiliation":[{"name":"DataThings S. A.,Luxembourg"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Djamila","family":"Aouada","sequence":"additional","affiliation":[{"name":"SnT, University of Luxembourg,Luxembourg"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"issue":"1","key":"ref1","first-page":"96","volume":"32","author":"Li","journal-title":"Learning IoT in Edge: Deep Learning for the Internet of Things with Edge Computing"},{"issue":"3","key":"ref2","first-page":"362","volume":"37","author":"Grigorescu","journal-title":"A survey of deep learning techniques for autonomous driving"},{"issue":"2","key":"ref3","first-page":"171","volume":"21","author":"Malamas","journal-title":"A survey on industrial vision systems, applications and tools"},{"issue":"12","key":"ref4","first-page":"44","volume":"35","author":"Furano","journal-title":"Towards the Use of Artificial Intelligence on the Edge in Space Systems: Challenges and Opportunities"},{"issue":"2","key":"ref5","first-page":"192","volume":"152","author":"Valera","journal-title":"Intelligent distributed surveillance systems: A review"},{"issue":"5","key":"ref6","first-page":"125","volume":"13","author":"V\u00e9stias","journal-title":"Moving Deep Learning to the Edge"},{"volume-title":"A Survey of Model Compression and Acceleration for Deep Neural Networks.","author":"Cheng","key":"ref7"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"issue":"3","key":"ref9","first-page":"211","volume":"115","author":"Russakovsky","journal-title":"ImageNet Large Scale Visual Recognition Challenge"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10602-1_48"},{"issue":"11","key":"ref11","first-page":"2278","volume":"86","author":"Lecun","journal-title":"Gradient-based learning applied to document recognition"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"author":"Simonyan","key":"ref13","article-title":"Very Deep Convolutional Networks for Large-Scale Image Recognition"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"author":"Blalock","key":"ref15","article-title":"What is the State of Neural Network Pruning?"},{"author":"Kim","key":"ref16","article-title":"Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile Applications"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1201\/9781003162810-13"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-021-01453-z"},{"key":"ref19","first-page":"1135","volume":"28","author":"Han","journal-title":"Learning both Weights and Connections for Efficient Neural Network"},{"author":"Li","key":"ref20","article-title":"Pruning Filters for Efficient ConvNets"},{"author":"Krishnamoorthi","key":"ref21","article-title":"Quantizing deep convolutional networks for efficient inference: A whitepaper"},{"journal-title":"Towards Efficient Tensor Decomposition-Based DNN Model Compression With Optimization Framework","first-page":"10 674","author":"Yin","key":"ref22"},{"key":"ref23","first-page":"3145","article-title":"Learning Important Features Through Propagating Activation Differences","volume-title":"Proceedings of the 34th International Conference on Machine Learning - Volume 70","author":"Shrikumar"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2018.00097"},{"journal-title":"Optimal Brain Damage","first-page":"8","author":"LeCun","key":"ref25"},{"author":"Tanaka","key":"ref26","article-title":"Pruning neural networks without any data by iteratively conserving synaptic flow"},{"journal-title":"Channel Pruning for Accelerating Very Deep Neural Networks","first-page":"1389","author":"He","key":"ref27"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.541"},{"issue":"3","key":"ref29","first-page":"455","volume":"51","author":"Kolda","journal-title":"Tensor Decompositions and Applications"},{"volume-title":"Speeding-up Convolutional Neural Networks Using Fine-tuned CP-Decomposition.","author":"Lebedev","key":"ref30"},{"volume-title":"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.","author":"Howard","key":"ref31"},{"journal-title":"Global Analytic Solution of Fully-observed Variational Bayesian Matrix Factorization","first-page":"37","author":"Nakajima","key":"ref32"},{"journal-title":"Training Quantized Nets: A Deeper Understanding","first-page":"11","author":"Li","key":"ref33"},{"author":"Chen","key":"ref34","article-title":"Training Deep Nets with Sublinear Memory Cost"},{"journal-title":"SuperNeurons: Dynamic GPU Memory Management for Training Deep Neural Networks","first-page":"41","author":"Wang","key":"ref35"},{"journal-title":"EFFICIENT INFERENCE WITH TEN-SORRT","first-page":"24","author":"Vanholder","key":"ref36"},{"key":"ref37","first-page":"8024","article-title":"PyTorch: An imperative style, high-performance deep learning library","volume-title":"Advances in Neural Information Processing Systems","volume":"32","author":"Paszke"},{"article-title":"The State of Machine Learning Frameworks in 2019","volume-title":"The Gradient","author":"He","key":"ref38"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-79463-7_43"},{"journal-title":"Learning Multiple Layers of Features from Tiny Images","first-page":"60","author":"Krizhevsky","key":"ref40"}],"event":{"name":"2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS)","start":{"date-parts":[[2022,8,1]]},"location":"Barcelona, Spain","end":{"date-parts":[[2022,8,3]]}},"container-title":["2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9854928\/9854933\/09854973.pdf?arnumber=9854973","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,24]],"date-time":"2024-01-24T03:28:43Z","timestamp":1706066923000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9854973\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,1]]},"references-count":40,"URL":"https:\/\/doi.org\/10.1109\/coins54846.2022.9854973","relation":{},"subject":[],"published":{"date-parts":[[2022,8,1]]}}}