How Int8 (byte) operations can be useful for deep learning?
Actually, recently people have been trying much lower precision in neural nets: 1-2-5 scheme (1 bit weights, 2 bit activations, and 5 bit gradients) seems to work well for easy datasets (MNIST and CIFAR-10). However, on ImageNet the results are significantly lower than those with full-precision (16 or 32 bit).
To achieve state of the art, convnets don't need more than 16 bits for training, but current RNNs might need more. For inference, on ImageNet, 4-5 bit weights (stochastically rounded from full precision) should be enough.