Neural network quantization has become an important research area due to its great impact on deployment of large models on resource constrained devices. In order to train networks that can be effectively discretized without loss of performance, we introduce a differentiable quantization procedure. Differentiability can be achieved by transforming continuous distributions over the weights and activations of the network to categorical distributions over the quantization grid. These are subsequently relaxed to continuous surrogates that can allow for efficient gradient-based optimization. We further show that stochastic rounding can be seen as a special case of the proposed approach and that under this formulation the quantization grid itself can also be optimized with gradient descent. We experimentally validate the performance of our method on MNIST, CIFAR 10 and Imagenet classification.
We introduce a technique that allows for gradient based training of quantized neural networks. Proposes a unified and general way of training neural networks with reduced precision quantized synaptic weights and activations. A new approach to quantizing activations which is state of the art or competitive on several real image problems. A method for learning neural networks with quantized weights and activations by stochastically quantizing values and replacing the resulting categotical distribution with a continuous relaxation