Hardware neural network accelerators

Abstract

Because of increasingly stringent energy constraints (e.g., Dark Silicon, there is a growing consensus in the community that we may be moving towards heterogeneous multi-core architectures, composed of a mix of cores and accelerators. Because our community is traditionally focused on general-purpose computing, we have been especially considering accelerator approaches such as GPUs and reconfigurable circuits. An attractive alternative is to investigate accelerators which are focused on a few key algorithms: key algorithms still mean broad application scope, but few algorithms enable energy efficient and cost-effective accelerators.