EPHA: An Energy-efficient Parallel Hybrid Architecture for ANNs and SNNs

Abstract

Artificial neural networks (ANNs) and spiking neural networks (SNNs) are two general approaches to achieve artificial intelligence (AI). The former have been widely used in academia and industry fields. The latter, SNNs, are more similar to biological neural networks and can realize ultra-low power consumption, thus have received widespread research attention. However, due to their fundamental differences in computation formula and information coding, the two methods often require different and incompatible platforms. Alongside the development of AI, a general platform that can support both ANNs and SNNs is necessary. Moreover, there are some similarities between ANNs and SNNs, which leaves room to deploy different networks on the same architecture. However, there is little related research on this topic. Accordingly, this paper presents an energy-efficient, scalable, and non-Von Neumann architecture (EPHA) for ANNs and SNNs. Our study combines device-, circuit-, architecture-, and algorithm-level innovations to achieve a parallel architecture with ultra-low power consumption. We use the compensated ferrimagnet to act as both synapses and neurons to store weights and perform dot-product operations, respectively. Moreover, we propose a novel computing flow to reduce the operations across multiple crossbar arrays, which enables our design to conduct large and complex tasks. On a suite of ANN and SNN workloads, the EPHA is 1.6 × more power efficient than a state-of-the-art design, NEBULA, in the ANN mode. In the SNN mode, our design is 4 orders of magnitude more than the Loihi in power efficiency.