Mapping Very Large Scale Spiking Neuron Network to Neuromorphic Hardware
Ouwen Jin, Qinghui Xing, Ying Li, Shuiguang Deng, Shuibing He, Gang Pan
Abstract
Neuromorphic hardware is a multi-core computer system specifically designed to run Spiking Neuron Network (SNN) applications. As the scale of neuromorphic hardware increases, it becomes very challenging to efficiently map a large SNN to hardware. In this paper, we proposed an efficient approach to map very large scale SNN applications to neuromorphic hardware, aiming to reduce energy consumption, spike latency, and on-chip network communication congestion. The approach consists of two steps. Firstly, it solves the initial placement using the Hilbert curve, a space-filling curve with unique properties that are particularly suitable for mapping SNNs. Secondly, the Force Directed (FD) algorithm is developed to optimize the initial placement. The FD algorithm formulates the connections of clusters as tension forces, thus converts the local optimization of placement as a force analysis problem. The proposed approach is evaluated with the scale of 4 billion neurons, which is more than 200 times larger than previous research. The results show that our approach achieves state-of-the-art performance, significantly exceeding existing approaches.