{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T10:33:50Z","timestamp":1771065230432,"version":"3.50.1"},"reference-count":24,"publisher":"IEEE","license":[{"start":{"date-parts":[[2019,7,1]],"date-time":"2019-07-01T00:00:00Z","timestamp":1561939200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2019,7,1]],"date-time":"2019-07-01T00:00:00Z","timestamp":1561939200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2019,7,1]],"date-time":"2019-07-01T00:00:00Z","timestamp":1561939200000},"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":[[2019,7]]},"DOI":"10.1109\/patmos.2019.8862162","type":"proceedings-article","created":{"date-parts":[[2019,10,10]],"date-time":"2019-10-10T23:12:11Z","timestamp":1570749131000},"page":"91-96","source":"Crossref","is-referenced-by-count":10,"title":["Minimizing Power for Neural Network Training with Logarithm-Approximate Floating-Point Multiplier"],"prefix":"10.1109","author":[{"given":"TaiYu","family":"Cheng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jaehoon","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Masanori","family":"Hashimoto","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref10","first-page":"7685","article-title":"Training deep neural networks with 8-bit floating point numbers","author":"wang","year":"2018","journal-title":"NIPS"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TETC.2017.2673548"},{"key":"ref12","article-title":"Energy table for 45nm process","author":"horowitz","year":"0"},{"key":"ref13","first-page":"444","article-title":"Energy efficient runtime approximate computing on data flow graphs","author":"gao","year":"2017","journal-title":"ICCAD"},{"key":"ref14","article-title":"Fourclass","author":"chang","year":"1996"},{"key":"ref15","article-title":"Neural Networks and Learning Machines","author":"haykin","year":"2008","journal-title":"PEARSON Education 3 edition"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ISSCC.2018.8310261"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2011.09.039"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1145\/3218603.3218621"},{"key":"ref19","article-title":"Rethinking floating point for deep learning","author":"johnson","year":"2018"},{"key":"ref4","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"NIPS ’13"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ICNP.2017.8117585"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/2627369.2627613"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/92.845894"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ISLPED.2015.7273495"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.7873\/DATE.2015.0618"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.14778\/3229863.3236256"},{"key":"ref1","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1145\/2831347.2831354","article-title":"Edge-centric Computing: Vision and Challenges","volume":"45","author":"pedro","year":"2015","journal-title":"ACM SIGCOMM Computer Communication Review"},{"key":"ref9","article-title":"Training deep neural networks with low precision multiplications","author":"david","year":"2014"},{"key":"ref20","article-title":"Mnist","author":"lecun","year":"1998"},{"key":"ref22","article-title":"ISOLET Data Set","year":"1994"},{"key":"ref21","article-title":"Human Activity Recognition Using Smartphones Data Set","year":"2012"},{"key":"ref24","article-title":"Digital implementation of the sigmoid function for FPGA circuits","author":"tisan","year":"2009","journal-title":"Acta Technica Napocensis Electronics and Telecommunications"},{"key":"ref23","article-title":"Machine learning","author":"murphy","year":"2012"}],"event":{"name":"2019 29th International Symposium on Power and Timing Modeling, Optimization and Simulation (PATMOS)","location":"Rhodes, Greece","start":{"date-parts":[[2019,7,1]]},"end":{"date-parts":[[2019,7,3]]}},"container-title":["2019 29th International Symposium on Power and Timing Modeling, Optimization and Simulation (PATMOS)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8854775\/8862002\/08862162.pdf?arnumber=8862162","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,17]],"date-time":"2022-07-17T21:53:21Z","timestamp":1658094801000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8862162\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7]]},"references-count":24,"URL":"https:\/\/doi.org\/10.1109\/patmos.2019.8862162","relation":{},"subject":[],"published":{"date-parts":[[2019,7]]}}}