A method, system and apparatus provide bit-sparse neural network optimization. Rather than quantizing and pruning weight and activation elements at the word level, weight and activation elements are pruned at the bit level, which reduces the density of effective “set” bits in weight and activation data, which, advantageously, reduces the power consumption of the neural network inference process by reducing the degree of bit-level switching during inference.