{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/binarized-neural-networks-training-deep","title":"Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1","arxiv_id":"1602.02830","date":"2016-02-09","proceeding":null,"authors":["Matthieu Courbariaux","Itay Hubara","Daniel Soudry","Ran El-Yaniv","Yoshua Bengio"],"abstract":"We introduce a method to train Binarized Neural Networks (BNNs) - neural\nnetworks with binary weights and activations at run-time. At training-time the\nbinary weights and activations are used for computing the parameters gradients.\nDuring the forward pass, BNNs drastically reduce memory size and accesses, and\nreplace most arithmetic operations with bit-wise operations, which is expected\nto substantially improve power-efficiency. To validate the effectiveness of\nBNNs we conduct two sets of experiments on the Torch7 and Theano frameworks. On\nboth, BNNs achieved nearly state-of-the-art results over the MNIST, CIFAR-10\nand SVHN datasets. Last but not least, we wrote a binary matrix multiplication\nGPU kernel with which it is possible to run our MNIST BNN 7 times faster than\nwith an unoptimized GPU kernel, without suffering any loss in classification\naccuracy. The code for training and running our BNNs is available on-line.","url_abs":"http://arxiv.org/abs/1602.02830v3","url_pdf":"http://arxiv.org/pdf/1602.02830v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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