Liquid State Machine Applications Mapping for NoC-Based Neuromorphic Platforms

Abstract

Liquid State Machine (LSM) is one of spiking neural network (SNN) containing recurrent connections in the reservoir. Nowadays, LSM is widely deployed on a variety of neuromorphic platforms to deal with vision and audio tasks. These platforms adopt Network-on-Chips (NoC) architecture for multi-cores interconnection. However, a large communication volume stemming from the reservoir of LSM has a significant effect on the performance of the platform. In this paper, we propose an LSM mapping method by using the toolchain - SNEAP for mapping LSM to neuromorphic platforms with multi-cores, which aims to reduce the energy and latency brought by spike communication on the interconnection. The method includes two key steps: partitioning the LSM to reduce the spikes communicated between partitions, and mapping the partitions of LSM to the NoC to reduce average hop of spikes under the constraint of hardware resources. This method is also effective for large-scale of LSM. The experimental results show that our method can achieve 1.5\({\times }\) reduction in end-to-end execution time, and reduce average energy consumption by 57% on 8 \(\times \) 8 2D-mesh NoC and average spike latency by 23% on 4 \(\times \) 4 2D-mesh NoC, compared to SpiNeMap.