{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,25]],"date-time":"2026-10-25T22:08:30Z","timestamp":1792966110742,"version":"4.5.0"},"publisher-location":"New York, NY, USA","reference-count":21,"publisher":"ACM","license":[{"start":{"date-parts":[[2019,6,2]],"date-time":"2019-06-02T00:00:00Z","timestamp":1559433600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2019,6,2]]},"DOI":"10.1145\/3316781.3317783","type":"proceedings-article","created":{"date-parts":[[2019,5,23]],"date-time":"2019-05-23T18:07:13Z","timestamp":1558634833000},"page":"1-6","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":29,"title":["BiScaled-DNN"],"prefix":"10.1145","author":[{"given":"Shubham","family":"Jain","sequence":"first","affiliation":[{"name":"School of Electrical and Computer Engineering, Purdue University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Swagath","family":"Venkataramani","sequence":"additional","affiliation":[{"name":"IBM T.J. Watson Research Center, Yorktown Heights, NY"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vijayalakshmi","family":"Srinivasan","sequence":"additional","affiliation":[{"name":"IBM T.J. Watson Research Center, Yorktown Heights, NY"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jungwook","family":"Choi","sequence":"additional","affiliation":[{"name":"IBM T.J. Watson Research Center, Yorktown Heights, NY"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kailash","family":"Gopalakrishnan","sequence":"additional","affiliation":[{"name":"IBM T.J. Watson Research Center, Yorktown Heights, NY"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leland","family":"Chang","sequence":"additional","affiliation":[{"name":"IBM T.J. Watson Research Center, Yorktown Heights, NY"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2019,6,2]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Neural Networks with Few Multiplications. CoRR, abs","author":"Lin Z","year":"2015","unstructured":"Z Lin Neural Networks with Few Multiplications. CoRR, abs , 2015 . Z Lin et al. Neural Networks with Few Multiplications. CoRR, abs, 2015."},{"key":"e_1_3_2_1_2_1","volume-title":"Ternary Weight Networks. CoRR, abs\/1605.04711","author":"Li F.","year":"2016","unstructured":"F. Li and B. Liu . Ternary Weight Networks. CoRR, abs\/1605.04711 , 2016 . F. Li and B. Liu. Ternary Weight Networks. CoRR, abs\/1605.04711, 2016."},{"key":"e_1_3_2_1_3_1","volume-title":"DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients. CoRR, abs\/1606.06160","author":"Zhou S","year":"2016","unstructured":"S Zhou DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients. CoRR, abs\/1606.06160 , 2016 . S Zhou et al. DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients. CoRR, abs\/1606.06160, 2016."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/2627369.2627613"},{"key":"e_1_3_2_1_5_1","volume-title":"PACT: parameterized clipping activation for quantized neural networks. CoRR, abs\/1805.06085","author":"J.","year":"2018","unstructured":"J. Choi et. al. PACT: parameterized clipping activation for quantized neural networks. CoRR, abs\/1805.06085 , 2018 . J. Choi et. al. PACT: parameterized clipping activation for quantized neural networks. CoRR, abs\/1805.06085, 2018."},{"key":"e_1_3_2_1_6_1","volume-title":"WRPN: wide reduced-precision networks. CoRR, abs\/1709.01134","author":"Mishra K.","year":"2017","unstructured":"Asit K. Mishra , Eriko Nurvitadhi, Jeffrey J. Cook , and Debbie Marr. WRPN: wide reduced-precision networks. CoRR, abs\/1709.01134 , 2017 . Asit K. Mishra, Eriko Nurvitadhi, Jeffrey J. Cook, and Debbie Marr. WRPN: wide reduced-precision networks. CoRR, abs\/1709.01134, 2017."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3061639.3062259"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISCA.2018.00063"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3195970.3196012"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"e_1_3_2_1_11_1","volume-title":"BinaryNet: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1. CoRR, abs\/1602.02830","author":"Courbariaux M.","year":"2016","unstructured":"M. Courbariaux BinaryNet: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1. CoRR, abs\/1602.02830 , 2016 . M. Courbariaux et al. BinaryNet: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1. CoRR, abs\/1602.02830, 2016."},{"key":"e_1_3_2_1_12_1","volume-title":"XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks. CoRR, abs\/1603.05279","author":"Rastegari M","year":"2016","unstructured":"M Rastegari XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks. CoRR, abs\/1603.05279 , 2016 . M Rastegari et al. XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks. CoRR, abs\/1603.05279, 2016."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/2925426.2926294"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.5555\/3130379.3130725"},{"key":"e_1_3_2_1_15_1","volume-title":"Deep compression: Compressing deep neural network with pruning, trained quantization and huffman coding. CoRR, abs","author":"Han S.","year":"2015","unstructured":"S. Han Deep compression: Compressing deep neural network with pruning, trained quantization and huffman coding. CoRR, abs , 2015 . S. Han et al. Deep compression: Compressing deep neural network with pruning, trained quantization and huffman coding. CoRR, abs, 2015."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3079856.3080244"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISCA.2016.32"},{"key":"e_1_3_2_1_18_1","volume-title":"Proc. VLSI Symposium","author":"B.","year":"2018","unstructured":"B. Fleischer et. al. A scalable multi-teraops deep learning processor core for ai training and inference . In Proc. VLSI Symposium , June 2018 . B. Fleischer et. al. A scalable multi-teraops deep learning processor core for ai training and inference. In Proc. VLSI Symposium, June 2018."},{"key":"e_1_3_2_1_19_1","volume-title":"Sparce: Sparsity aware general purpose core extensions to accelerate deep neural networks. CoRR, abs\/1711.06315","author":"Sen S","year":"2017","unstructured":"S Sen Sparce: Sparsity aware general purpose core extensions to accelerate deep neural networks. CoRR, abs\/1711.06315 , 2017 . S Sen et al. Sparce: Sparsity aware general purpose core extensions to accelerate deep neural networks. CoRR, abs\/1711.06315, 2017."},{"key":"e_1_3_2_1_20_1","volume-title":"Caffe: Convolutional Architecture for Fast Feature Embedding. arXiv preprint arXiv:1408.5093","author":"Jia Y","year":"2014","unstructured":"Y Jia Caffe: Convolutional Architecture for Fast Feature Embedding. arXiv preprint arXiv:1408.5093 , 2014 . Y Jia et al. Caffe: Convolutional Architecture for Fast Feature Embedding. arXiv preprint arXiv:1408.5093, 2014."},{"key":"e_1_3_2_1_21_1","volume-title":"Proc. PACT","author":"Poster S.","year":"2017","unstructured":"S. Venkataramani et. al. Poster : Design space exploration for performance optimization of deep neural networks on shared memory accelerators . In Proc. PACT , 2017 . S. Venkataramani et. al. Poster: Design space exploration for performance optimization of deep neural networks on shared memory accelerators. In Proc. PACT, 2017."}],"event":{"name":"DAC '19: The 56th Annual Design Automation Conference 2019","location":"Las Vegas NV USA","acronym":"DAC '19","sponsor":["SIGDA ACM Special Interest Group on Design Automation","IEEE-CEDA","SIGBED ACM Special Interest Group on Embedded Systems"]},"container-title":["Proceedings of the 56th Annual Design Automation Conference 2019"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3316781.3317783","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3316781.3317783","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T01:08:03Z","timestamp":1750208883000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3316781.3317783"}},"subtitle":["Quantizing Long-tailed Datastructures with Two Scale Factors for Deep Neural Networks"],"short-title":[],"issued":{"date-parts":[[2019,6,2]]},"references-count":21,"alternative-id":["10.1145\/3316781.3317783","10.1145\/3316781"],"URL":"https:\/\/doi.org\/10.1145\/3316781.3317783","relation":{},"subject":[],"published":{"date-parts":[[2019,6,2]]},"assertion":[{"value":"2019-06-02","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}