{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T11:31:02Z","timestamp":1780054262586,"version":"3.54.0"},"publisher-location":"New York, NY, USA","reference-count":27,"publisher":"ACM","license":[{"start":{"date-parts":[[2018,11,5]],"date-time":"2018-11-05T00:00:00Z","timestamp":1541376000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Compagnia di San Paolo"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2018,11,5]]},"DOI":"10.1145\/3240765.3240845","type":"proceedings-article","created":{"date-parts":[[2018,11,6]],"date-time":"2018-11-06T13:36:57Z","timestamp":1541511417000},"page":"1-8","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["Scalable-effort ConvNets for multilevel classification"],"prefix":"10.1145","author":[{"given":"Valentino","family":"Peluso","sequence":"first","affiliation":[{"name":"Politecnico di Torino, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrea","family":"Calimera","sequence":"additional","affiliation":[{"name":"Politecnico di Torino, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2018,11,5]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"d.}. Retrieved","year":"2018","unstructured":"{n. d.}. Retrieved April 16, 2018 from https:\/\/github.com\/ericsun99\/MobileNet-V2-Pytorch"},{"key":"e_1_3_2_1_2_1","volume-title":"Retrieved","year":"2018","unstructured":"{n. d.}. Fixed Point Quantization. Retrieved April 16, 2018 from https:\/\/www.tensorflow.org\/performance\/quantization"},{"key":"e_1_3_2_1_3_1","volume-title":"d.}. Pytorch Torchvision. Retrieved","year":"2018","unstructured":"{n. d.}. Pytorch Torchvision. Retrieved April 16, 2018 from http:\/\/pytorch.org\/docs\/master\/torchvision"},{"key":"e_1_3_2_1_4_1","volume-title":"VLSI(ISVLSI), 2016 IEEE Computer Society Annual Symposium on. IEEE, 236--241","author":"Renzo","unstructured":"Renzo Andri et al. 2016. YodaNN: An ultra-low power convolutional neural network accelerator based on binary weights. In VLSI(ISVLSI), 2016 IEEE Computer Society Annual Symposium on. IEEE, 236--241."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"crossref","unstructured":"Alsallakh Bilal et al. 2018. Do convolutional neural networks learn class hierarchy? IEEE transactions on visualization and computer graphics 24 1 (2018) 152--162.","DOI":"10.1109\/TVCG.2017.2744683"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/2342509.2342513"},{"key":"e_1_3_2_1_7_1","first-page":"11","article-title":"Origami: A 803-GOp\/s\/W Convolutional Network Accelerator","volume":"27","author":"Lukas Cavigelli","year":"2017","unstructured":"Lukas Cavigelli et al. 2017. Origami: A 803-GOp\/s\/W Convolutional Network Accelerator. IEEE Transactions on Circuits and Systems for Video Technology 27, 11 (Nov 2017), 2461--2475.","journal-title":"IEEE Transactions on Circuits and Systems for Video Technology"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSSC.2016.2616357"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.5555\/2969442.2969588"},{"key":"e_1_3_2_1_10_1","volume-title":"Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on. IEEE, 248--255","author":"Jia","unstructured":"Jia Deng et al. 2009. Imagenet: A large-scale hierarchical image database. In Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on. IEEE, 248--255."},{"key":"e_1_3_2_1_11_1","unstructured":"Philipp Gysel et al. 2016. Hardware-oriented approximation of convolutional neural networks. arXiv preprint arXiv:1604.03168 (2016)."},{"key":"e_1_3_2_1_12_1","unstructured":"Itay Hubara et al. 2016. Quantized neural networks: Training neural networks with low precision weights and activations. arXiv preprint arXiv:1609.07061 (2016)."},{"key":"e_1_3_2_1_13_1","unstructured":"Forrest N Iandola et al. 2016. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size. arXiv preprint arXiv:1602.07360 (2016)."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3079856.3080246"},{"key":"e_1_3_2_1_15_1","volume-title":"Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980","author":"Diederik Kingma","year":"2014","unstructured":"Diederik Kingma et al. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.5555\/515982"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","unstructured":"Alex Krizhevsky et al. 2012. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems. 1097--1105.","DOI":"10.5555\/2999134.2999257"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","unstructured":"Clive Maxfield et al. 2005. The definitive guide to how computers do math: featuring the virtual DIY calculator. Vol. 1. John Wiley & Sons.","DOI":"10.5555\/1088961"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/219717.219748"},{"key":"e_1_3_2_1_20_1","volume-title":"Applications of Computer Vision (WACV), 2016 IEEE Winter Conference on. IEEE, 1--8.","author":"Bert","unstructured":"Bert Moons et al. 2016. Energy-efficient convnets through approximate computing. In Applications of Computer Vision (WACV), 2016 IEEE Winter Conference on. IEEE, 1--8."},{"key":"e_1_3_2_1_21_1","volume-title":"Solid-State Circuits Conference (ISSCC)","author":"Bert","year":"2017","unstructured":"Bert Moons et al. 2017. 14.5 envision: A 0.26-to-10tops\/w subword-parallel dynamic-voltage-accuracy-frequency-scalable convolutional neural network processor in 28nm fdsoi. In Solid-State Circuits Conference (ISSCC), 2017 IEEE International. IEEE, 246--247."},{"key":"e_1_3_2_1_22_1","volume-title":"Weak-MAC: Arithmetic Relaxation for Dynamic Energy-Accuracy Scaling in ConvNets. In Circuits and Systems (ISCAS), 2018 IEEE International Symposium on. IEEE, 1--5.","author":"Valentino","unstructured":"Valentino Peluso et al. 2018. Weak-MAC: Arithmetic Relaxation for Dynamic Energy-Accuracy Scaling in ConvNets. In Circuits and Systems (ISCAS), 2018 IEEE International Symposium on. IEEE, 1--5."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"crossref","unstructured":"Mark Sandler et al. 2018. Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification Detection and Segmentation. arXiv preprint arXiv:1801.04381 (2018).","DOI":"10.1109\/CVPR.2018.00474"},{"key":"e_1_3_2_1_25_1","volume-title":"Speech and Signal Processing (ICASSP), 2017 IEEE International Conference on. IEEE, 2861--2865","author":"Ganesh","unstructured":"Ganesh Venkatesh et al. 2017. Accelerating deep convolutional networks using low-precision and sparsity. In Acoustics, Speech and Signal Processing (ICASSP), 2017 IEEE International Conference on. IEEE, 2861--2865."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.314"},{"key":"e_1_3_2_1_27_1","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 5687--5695","author":"Tien-Ju","unstructured":"Tien-Ju Yang et al. 2017. Designing Energy-Efficient Convolutional Neural Networks Using Energy-Aware Pruning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 5687--5695."}],"event":{"name":"ICCAD '18: IEEE\/ACM INTERNATIONAL CONFERENCE ON COMPUTER-AIDED DESIGN","location":"San Diego California","acronym":"ICCAD '18","sponsor":["IEEE-EDS Electronic Devices Society","IEEE CAS","IEEE CEDA"]},"container-title":["Proceedings of the International Conference on Computer-Aided Design"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3240765.3240845","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3240765.3240845","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T00:57:33Z","timestamp":1750208253000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3240765.3240845"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,11,5]]},"references-count":27,"alternative-id":["10.1145\/3240765.3240845","10.1145\/3240765"],"URL":"https:\/\/doi.org\/10.1145\/3240765.3240845","relation":{},"subject":[],"published":{"date-parts":[[2018,11,5]]},"assertion":[{"value":"2018-11-05","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}