  Deep neural networks are the state-of-the-art methods for many real-world
tasks, such as computer vision, natural language processing and speech
recognition. For all its popularity, deep neural networks are also criticized
for consuming a lot of memory and draining battery life of devices during
training and inference. This makes it hard to deploy these models on mobile or
embedded devices which have tight resource constraints. Quantization is
recognized as one of the most effective approaches to satisfy the extreme
memory requirements that deep neural network models demand. Instead of adopting
32-bit floating point format to represent weights, quantized representations
store weights using more compact formats such as integers or even binary
numbers. Despite a possible degradation in predictive performance, quantization
provides a potential solution to greatly reduce the model size and the energy
consumption. In this survey, we give a thorough review of different aspects of
quantized neural networks. Current challenges and trends of quantized neural
networks are also discussed.
