  As neural networks have become more powerful, there has been a rising desire
to deploy them in the real world; however, the power and accuracy of neural
networks is largely due to their depth and complexity, making them difficult to
deploy, especially in resource-constrained devices. Neural network quantization
has recently arisen to meet this demand of reducing the size and complexity of
neural networks by reducing the precision of a network. With smaller and
simpler networks, it becomes possible to run neural networks within the
constraints of their target hardware. This paper surveys the many neural
network quantization techniques that have been developed in the last decade.
Based on this survey and comparison of neural network quantization techniques,
we propose future directions of research in the area.
