{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,6]],"date-time":"2024-09-06T12:40:08Z","timestamp":1725626408298},"reference-count":24,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,9,5]],"date-time":"2022-09-05T00:00:00Z","timestamp":1662336000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,9,5]],"date-time":"2022-09-05T00:00:00Z","timestamp":1662336000000},"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":[[2022,9,5]]},"DOI":"10.1109\/socc56010.2022.9908072","type":"proceedings-article","created":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T20:23:18Z","timestamp":1665433398000},"page":"1-6","source":"Crossref","is-referenced-by-count":0,"title":["Efficient Hardware Approximation for Bit-Decomposition Based Deep Neural Network Accelerators"],"prefix":"10.1109","author":[{"given":"Taha","family":"Soliman","sequence":"first","affiliation":[{"name":"Robert Bosch GmbH,Renningen,Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amro","family":"Eldebiky","sequence":"additional","affiliation":[{"name":"Technische Universit&#x00E4;t M&#x00FC;nchen,M&#x00FC;nchen,Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cecilia","family":"De La Parra","sequence":"additional","affiliation":[{"name":"Robert Bosch GmbH,Renningen,Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andre","family":"Guntoro","sequence":"additional","affiliation":[{"name":"Robert Bosch GmbH,Renningen,Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Norbert","family":"Wehn","sequence":"additional","affiliation":[{"name":"TU Kaiserslautern,Kaiserslautern,Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ESSCIRC.2019.8902824"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/SOCC49529.2020.9524750"},{"key":"ref12","article-title":"Ultra-low power flexible precision fefet based analog in-memory computing","author":"taha","year":"2020","journal-title":"IEDM 2020"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/LSSC.2019.2937440"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/3007787.3001139"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN48605.2020.9206672"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2905138"},{"journal-title":"DATE 2020","article-title":"Proxsim: Gpu-based simulation framework for cross-layer approximate dnn optimization","year":"0","key":"ref17"},{"article-title":"TensorFlow: Large-scale machine learning on heterogeneous systems","year":"2015","author":"abadi","key":"ref18"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00141"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.23919\/DATE.2019.8714901"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.7873\/DATE.2015.0618"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/2627369.2627613"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/AICAS51828.2021.9458456"},{"key":"ref7","article-title":"Leveraging the error resilience of machine-learning applications for designing highly energy efficient accelerators","author":"du","year":"0","journal-title":"2014 ASP-DAC"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00826"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/MSSC.2017.2745818"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/LCA.2016.2597140"},{"key":"ref20","article-title":"Inverted residuals and linear bottlenecks: Mobile net-works for classification, detection and segmentation","author":"sandler","year":"2018","journal-title":"IEEE Conference on Computer Vision and Pattern Recognition"},{"key":"ref22","article-title":"Squeezenet: Alexnet-level accuracy with 50x fewer parameters and <1mb model size","author":"iandola","year":"0","journal-title":"ICLR&#x2019;19"},{"journal-title":"Learning multiple layers of features from tiny images","year":"2009","author":"krizhevsky","key":"ref21"},{"article-title":"Trained quantization thresholds for accurate and efficient fixed-point inference of deep neural networks","year":"2020","author":"jain","key":"ref24"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"}],"event":{"name":"2022 IEEE 35th International System-on-Chip Conference (SOCC)","start":{"date-parts":[[2022,9,5]]},"location":"Belfast, United Kingdom","end":{"date-parts":[[2022,9,8]]}},"container-title":["2022 IEEE 35th International System-on-Chip Conference (SOCC)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9907764\/9908069\/09908072.pdf?arnumber=9908072","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T20:02:18Z","timestamp":1675108938000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9908072\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,5]]},"references-count":24,"URL":"https:\/\/doi.org\/10.1109\/socc56010.2022.9908072","relation":{},"subject":[],"published":{"date-parts":[[2022,9,5]]}}}