Power-Efficient Accelerator Design for Neural Networks Using Computation Reuse

Applications of neural networks in various fields of research and technology have expanded widely in recent years. In particular, applications with inherent tolerance to accuracy loss, such as signal processing and multimedia applications, are highly suited to the approximation property of neural networks. This approximation property has been exploited in many existing neural network accelerators to trade-off accuracy for power-efficiency and speed. In addition to the power saving obtained by approximation, we observed that a considerable amount of arithmetic operations in neural networks are repetitive and can be eliminated to further decrease power consumption. Given this observation, we propose CORN, COmputation Reuse-aware Neural network accelerator that allows neurons to share their computation results, effectively eliminating the power usage of redundant computations. We will show that CORN lowers power consumption by 26 percent on average over low-power neural network accelerators.