{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T01:56:48Z","timestamp":1775267808916,"version":"3.50.1"},"reference-count":20,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,9,6]],"date-time":"2021-09-06T00:00:00Z","timestamp":1630886400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,9,6]],"date-time":"2021-09-06T00:00:00Z","timestamp":1630886400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,9,6]],"date-time":"2021-09-06T00:00:00Z","timestamp":1630886400000},"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":[[2021,9,6]]},"DOI":"10.1109\/rtsi50628.2021.9597341","type":"proceedings-article","created":{"date-parts":[[2021,11,15]],"date-time":"2021-11-15T23:04:55Z","timestamp":1637017495000},"page":"165-170","source":"Crossref","is-referenced-by-count":5,"title":["A QKeras Neural Network Zoo for Deeply Quantized Imaging"],"prefix":"10.1109","author":[{"given":"Francesco","family":"Loro","sequence":"first","affiliation":[]},{"given":"Danilo","family":"Pau","sequence":"additional","affiliation":[]},{"given":"Valeria","family":"Tomaselli","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref10","article-title":"Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients","volume":"absi1606 6160","author":"zhou","year":"2016","journal-title":"CoRR"},{"key":"ref11","author":"han","year":"2016","journal-title":"Deep compression Compressing deep neural networks with pruning trained quantization and huffman coding"},{"key":"ref12","first-page":"2876","article-title":"Software 2. 0 and snorkel: beyond hand-labeled data","author":"r\u00e9","year":"0","journal-title":"ACM SIGKDD International Conference on Knowledge Discovery and Data Mining"},{"key":"ref13","article-title":"Back to simplicity: How to train accurate bnns from scratch?","volume":"abs 1906 8637","author":"bethge","year":"2019","journal-title":"CoRR"},{"key":"ref14","article-title":"Learning to train a binary neural network","volume":"abs 1809 10463","author":"bethge","year":"2018","journal-title":"CoRR"},{"key":"ref15","author":"yu","year":"2015","journal-title":"Multi-scale context aggregation by dilated convolutions"},{"key":"ref16","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"krizhevsky","year":"2012","journal-title":"Neural Information Processing Systems"},{"key":"ref17","article-title":"Bi-narized neural networks","volume":"29","author":"hubara","year":"2016","journal-title":"Advances in neural information processing systems"},{"key":"ref18","article-title":"Training binary neural networks with real-to-binary convolutions","author":"martinez","year":"0","journal-title":"International Conference on Learning Representations"},{"key":"ref19","article-title":"Larq compute engine: Design, benchmark, and deploy state-of-the-art binarized neural networks","volume":"abs 2011 9398","author":"bannink","year":"2020","journal-title":"CoRR"},{"key":"ref4","article-title":"A survey on methods and theories of quantized neural networks","volume":"abs 1808 4752","author":"guo","year":"2018","journal-title":"CoRR"},{"key":"ref3","author":"chen","year":"2020","journal-title":"Tensorflow official model garden"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref5","article-title":"Binaryconnect: Training deep neural networks with binary weights during propagations","volume":"abs 1511 363","author":"courbariaux","year":"2015","journal-title":"CoRR"},{"key":"ref8","article-title":"Xnor-net: Imagenet classification using binary convolutional neural networks","volume":"abs 1603 5279","author":"rastegari","year":"2016","journal-title":"CoRR"},{"key":"ref7","article-title":"Learning multiple layers of features from tiny images","author":"krizhevsky","year":"2012","journal-title":"University of Toronto"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCE50685.2021.9427638"},{"key":"ref1","article-title":"Xcel-ram: Accelerating binary neural networks in high-throughput SRAM compute arrays","volume":"abs 1807 343","author":"agrawal","year":"2018","journal-title":"CoRR"},{"key":"ref9","article-title":"Imagenet large scale visual recognition challenge","volume":"abs 1409 575","author":"russakovsky","year":"2014","journal-title":"CoRR"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"}],"event":{"name":"2021 IEEE 6th International Forum on Research and Technology for Society and Industry (RTSI)","location":"Naples, Italy","start":{"date-parts":[[2021,9,6]]},"end":{"date-parts":[[2021,9,9]]}},"container-title":["2021 IEEE 6th International Forum on Research and Technology for Society and Industry (RTSI)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9597204\/9597208\/09597341.pdf?arnumber=9597341","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T16:50:49Z","timestamp":1652201449000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9597341\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,6]]},"references-count":20,"URL":"https:\/\/doi.org\/10.1109\/rtsi50628.2021.9597341","relation":{},"subject":[],"published":{"date-parts":[[2021,9,6]]}}}