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We introduce a method to train Binarized Neural Networks (BNNs) - neuralnetworks with binary weights and activations at run-time. At training-time thebinary weights and activations are used for computing the parameters gradients.During the forward pass, BNNs drastically reduce memory size and accesses, andreplace most arithmetic operations with bit-wise operations, which is expectedto substantially improve power-efficiency. To validate the effectiveness ofBNNs we conduct two sets of experiments on the Torch7 and Theano frameworks. Onboth, BNNs achieved nearly state-of-the-art results over the MNIST, CIFAR-10and SVHN datasets. Last but not least, we wrote a binary matrix multiplicationGPU kernel with which it is possible to run our MNIST BNN 7 times faster thanwith an unoptimized GPU kernel, without suffering any loss in classificationaccuracy. The code for training and running our BNNs is available on-line.

Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1