{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T11:24:07Z","timestamp":1782818647620,"version":"3.54.5"},"publisher-location":"New York, NY, USA","reference-count":19,"publisher":"ACM","license":[{"start":{"date-parts":[[2018,7,23]],"date-time":"2018-07-23T00:00:00Z","timestamp":1532304000000},"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":[[2018,7,23]]},"DOI":"10.1145\/3218603.3218615","type":"proceedings-article","created":{"date-parts":[[2019,2,19]],"date-time":"2019-02-19T20:59:18Z","timestamp":1550609958000},"page":"1-6","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":31,"title":["A Fully Onchip Binarized Convolutional Neural Network FPGA Impelmentation with Accurate Inference"],"prefix":"10.1145","author":[{"given":"Li","family":"Yang","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Orlando, Florida"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhezhi","family":"He","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Orlando, Florida"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Deliang","family":"Fan","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Orlando, Florida"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2018,7,23]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"European Conference on Computer Vision. Springer, 525--542","year":"2016","unstructured":"others. 2016 . Xnor-net: Imagenet classification using binary convolutional neural networks . In European Conference on Computer Vision. Springer, 525--542 . others. 2016. Xnor-net: Imagenet classification using binary convolutional neural networks. In European Conference on Computer Vision. Springer, 525--542."},{"key":"e_1_3_2_1_2_1","unstructured":"Yoshua Bengio et al. 2013. Estimating or propagating gradients through stochastic neurons for conditional computation. arXiv preprint arXiv:1308.3432 (2013).  Yoshua Bengio et al. 2013. Estimating or propagating gradients through stochastic neurons for conditional computation. arXiv preprint arXiv:1308.3432 (2013)."},{"key":"e_1_3_2_1_3_1","volume-title":"Binaryconnect: Training deep neural networks with binary weights during propagations. In Advances in neural information processing systems. 3123--3131.","author":"Matthieu Courbariaux","year":"2015","unstructured":"Matthieu Courbariaux et al. 2015 . Binaryconnect: Training deep neural networks with binary weights during propagations. In Advances in neural information processing systems. 3123--3131. Matthieu Courbariaux et al. 2015. Binaryconnect: Training deep neural networks with binary weights during propagations. In Advances in neural information processing systems. 3123--3131."},{"key":"e_1_3_2_1_4_1","unstructured":"Matthieu Courbariaux et al. 2016. Binarized neural networks: Training deep neural networks with weights and activations constrained to+ 1 or-1. arXiv preprint arXiv:1602.02830 (2016).  Matthieu Courbariaux et al. 2016. Binarized neural networks: Training deep neural networks with weights and activations constrained to+ 1 or-1. arXiv preprint arXiv:1602.02830 (2016)."},{"key":"e_1_3_2_1_5_1","unstructured":"Song Han et al. 2015. Deep compression: Compressing deep neural networks with pruning trained quantization and huffman coding. arXiv preprint arXiv:1510.00149 (2015).  Song Han et al. 2015. Deep compression: Compressing deep neural networks with pruning trained quantization and huffman coding. arXiv preprint arXiv:1510.00149 (2015)."},{"key":"e_1_3_2_1_6_1","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition. 770--778","author":"Kaiming","unstructured":"Kaiming He et al. 2016. Deep residual learning for image recognition . In Proceedings of the IEEE conference on computer vision and pattern recognition. 770--778 . Kaiming He et al. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition. 770--778."},{"key":"e_1_3_2_1_7_1","volume-title":"International conference on machine learning. 448--456","author":"Sergey","unstructured":"Sergey Ioffe et al. 2015. Batch normalization: Accelerating deep network training by reducing internal covariate shift . In International conference on machine learning. 448--456 . Sergey Ioffe et al. 2015. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International conference on machine learning. 448--456."},{"key":"e_1_3_2_1_8_1","volume-title":"Computer Vision and Pattern Recognition (CVPR), 2017 IEEE Conference on","volume":"1","author":"Felix","unstructured":"Felix Juefei-Xu et al. 2017. Local binary convolutional neural networks . In Computer Vision and Pattern Recognition (CVPR), 2017 IEEE Conference on , Vol. 1 . Felix Juefei-Xu et al. 2017. Local binary convolutional neural networks. In Computer Vision and Pattern Recognition (CVPR), 2017 IEEE Conference on, Vol. 1."},{"key":"e_1_3_2_1_9_1","unstructured":"Alex Krizhevsky et al. 2012. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems. 1097--1105.   Alex Krizhevsky et al. 2012. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems. 1097--1105."},{"key":"e_1_3_2_1_10_1","unstructured":"Alex Krizhevsky et al. 2014. The CIFAR-10 dataset. online: http:\/\/www.cs.toronto.edu\/kriz\/cifar. html (2014).  Alex Krizhevsky et al. 2014. The CIFAR-10 dataset. online: http:\/\/www.cs.toronto.edu\/kriz\/cifar. html (2014)."},{"key":"e_1_3_2_1_11_1","unstructured":"Xiaofan Lin et al. 2017. Towards Accurate Binary Convolutional Neural Network. In Advances in Neural Information Processing Systems. 344--352.  Xiaofan Lin et al. 2017. Towards Accurate Binary Convolutional Neural Network. In Advances in Neural Information Processing Systems. 344--352."},{"key":"e_1_3_2_1_12_1","volume-title":"Field Programmable Logic and Applications (FPL), 2017 27th International Conference on. IEEE, 1--4.","author":"Hiroki","unstructured":"Hiroki Nakahara et al. 2017. A fully connected layer elimination for a binarizec convolutional neural network on an FPGA . In Field Programmable Logic and Applications (FPL), 2017 27th International Conference on. IEEE, 1--4. Hiroki Nakahara et al. 2017. A fully connected layer elimination for a binarizec convolutional neural network on an FPGA. In Field Programmable Logic and Applications (FPL), 2017 27th International Conference on. IEEE, 1--4."},{"key":"e_1_3_2_1_13_1","unstructured":"Yuval Netzer et al. 2011. Reading digits in natural images with unsupervised feature learning Vol. 2011. 5.  Yuval Netzer et al. 2011. Reading digits in natural images with unsupervised feature learning Vol. 2011. 5."},{"key":"e_1_3_2_1_14_1","unstructured":"Adam Paszke et al. 2017. Pytorch. (2017).  Adam Paszke et al. 2017. Pytorch. (2017)."},{"key":"e_1_3_2_1_15_1","unstructured":"Wonyong Sung et al. 2015. Resiliency of deep neural networks under quantization. arXiv preprint arXiv:1511.06488 (2015).  Wonyong Sung et al. 2015. Resiliency of deep neural networks under quantization. arXiv preprint arXiv:1511.06488 (2015)."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"crossref","unstructured":"Wei Tang et al. 2017. How to train a compact binary neural network with high accuracy?  Wei Tang et al. 2017. How to train a compact binary neural network with high accuracy?","DOI":"10.1609\/aaai.v31i1.10862"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3020078.3021744"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3020078.3021741"},{"key":"e_1_3_2_1_19_1","unstructured":"Shuchang Zhou et al. 2016. DoReFa-Net: Training low bitwidth convolutional neural networks with low bitwidth gradients. arXiv preprint arXiv:1606.06160 (2016).  Shuchang Zhou et al. 2016. DoReFa-Net: Training low bitwidth convolutional neural networks with low bitwidth gradients. arXiv preprint arXiv:1606.06160 (2016)."}],"event":{"name":"ISLPED '18: International Symposium on Low Power Electronics and Design","location":"Seattle WA USA","acronym":"ISLPED '18","sponsor":["SIGDA ACM Special Interest Group on Design Automation","IEEE CAS"]},"container-title":["Proceedings of the International Symposium on Low Power Electronics and Design"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3218603.3218615","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3218603.3218615","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T22:29:30Z","timestamp":1750285770000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3218603.3218615"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,7,23]]},"references-count":19,"alternative-id":["10.1145\/3218603.3218615","10.1145\/3218603"],"URL":"https:\/\/doi.org\/10.1145\/3218603.3218615","relation":{},"subject":[],"published":{"date-parts":[[2018,7,23]]},"assertion":[{"value":"2018-07-23","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}