  Deep neural networks have proven to be particularly effective in visual and
audio recognition tasks. Existing models tend to be computationally expensive
and memory intensive, however, and so methods for hardware-oriented
approximation have become a hot topic. Research has shown that custom
hardware-based neural network accelerators can surpass their general-purpose
processor equivalents in terms of both throughput and energy efficiency.
Application-tailored accelerators, when co-designed with approximation-based
network training methods, transform large, dense and computationally expensive
networks into small, sparse and hardware-efficient alternatives, increasing the
feasibility of network deployment. In this article, we provide a comprehensive
evaluation of approximation methods for high-performance network inference
along with in-depth discussion of their effectiveness for custom hardware
implementation. We also include proposals for future research based on a
thorough analysis of current trends. This article represents the first survey
providing detailed comparisons of custom hardware accelerators featuring
approximation for both convolutional and recurrent neural networks, through
which we hope to inspire exciting new developments in the field.
