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