{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T23:26:08Z","timestamp":1780356368489,"version":"3.54.1"},"reference-count":30,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,5,28]],"date-time":"2022-05-28T00:00:00Z","timestamp":1653696000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,5,28]],"date-time":"2022-05-28T00:00:00Z","timestamp":1653696000000},"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":[[2022,5,28]]},"DOI":"10.1109\/iscas48785.2022.9937676","type":"proceedings-article","created":{"date-parts":[[2022,11,11]],"date-time":"2022-11-11T20:38:08Z","timestamp":1668199088000},"page":"1650-1654","source":"Crossref","is-referenced-by-count":4,"title":["ConfAx: Exploiting Approximate Computing for Configurable FPGA CNN Acceleration at the Edge"],"prefix":"10.1109","author":[{"given":"Guilherme","family":"Korol","sequence":"first","affiliation":[{"name":"Institute of Informatics - Universidade Federal do Rio Grande do Sul (UFRGS),Porto Alegre,Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael Guilherme","family":"Jordan","sequence":"additional","affiliation":[{"name":"Institute of Informatics - Universidade Federal do Rio Grande do Sul (UFRGS),Porto Alegre,Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mateus Beck","family":"Rutzig","sequence":"additional","affiliation":[{"name":"Universidade Federal de Santa Maria (UFSM),Electronics and Computing Department,Santa Maria,Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antonio Carlos Schneider","family":"Beck","sequence":"additional","affiliation":[{"name":"Institute of Informatics - Universidade Federal do Rio Grande do Sul (UFRGS),Porto Alegre,Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref30","article-title":"Learning multiple layers of features from tiny images","author":"krizhevsky","year":"2009","journal-title":"Technical Report"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ASAP49362.2020.00040"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/3476990"},{"key":"ref12","first-page":"1135","article-title":"Learning both weights and connections for efficient neural networks","author":"han","year":"2015","journal-title":"NIPS"},{"key":"ref13","article-title":"Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding","author":"han","year":"2016","journal-title":"ArXiv"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TETC.2021.3050989"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/2684746.2689060"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2875376"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/2966986.2967021"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2889110"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/MWSCAS48704.2020.9184640"},{"key":"ref28","first-page":"8024","article-title":"Pytorch: An imperative style, high-performance deep learning library","author":"gross","year":"2019","journal-title":"NeurIPS"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2018.2842821"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/DAC.2018.8465845"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2017.1700168"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/3020078.3021740"},{"key":"ref29","first-page":"1106","article-title":"Imagenet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"NIPS"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2013.257"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM41043.2020.9155435"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2018.8485850"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/2789168.2790123"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TCSII.2021.3066309"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2019.2921977"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/LASCAS45839.2020.9069040"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1145\/2627369.2627613"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ReCoSoC48741.2019.9034956"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2018.2880742"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.7873\/DATE.2015.0618"},{"key":"ref26","article-title":"Xilinx Real-Time Video Server Appliance","author":"inc","year":"2021"},{"key":"ref25","article-title":"A HLS-based Deep Neural Network Accelerator library for Xilinx Ultrascale+ MPSoC devices","author":"inc","year":"2020"}],"event":{"name":"2022 IEEE International Symposium on Circuits and Systems (ISCAS)","location":"Austin, TX, USA","start":{"date-parts":[[2022,5,27]]},"end":{"date-parts":[[2022,6,1]]}},"container-title":["2022 IEEE International Symposium on Circuits and Systems (ISCAS)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9937201\/9937203\/09937676.pdf?arnumber=9937676","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,12]],"date-time":"2022-12-12T19:57:50Z","timestamp":1670875070000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9937676\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,28]]},"references-count":30,"URL":"https:\/\/doi.org\/10.1109\/iscas48785.2022.9937676","relation":{},"subject":[],"published":{"date-parts":[[2022,5,28]]}}}