Event-based computation has recently gained increasing research interest for applications of vision recognition due to its intrinsic advantages on efficiency and speed. However, the existing event-based models for vision recognition are faced with several issues, such as large network complexity and expensive training cost. In this paper, we propose an improved multi-liquid state machine (M-LSM) method for high-performance vision recognition. Specifically, we introduce two methods, namely multi-state fusion and multi-liquid search, to optimize the liquid state machine (LSM). Multi-state fusion by sampling the liquid state at multiple timesteps could reserve richer spatiotemporal information. We adapt network architecture search (NAS) to find the potential optimal architecture of the multi-liquid state machine. We also train the M-LSM through an unsupervised learning rule spike-timing dependent plasticity (STDP). Our M-LSM is evaluated on two event-based datasets and demonstrates state-of-the-art recognition performance with superior advantages on network complexity and training cost.

Network-on-Chip (NoC) is widely adopted in neuromorphic processors to support communication between neurons in spiking neural networks (SNNs). However, SNNs generate enormous spiking packets due to the one-to-many traffic pattern. The spiking packets may cause communication pressure on NoC. We propose a path-based multicast routing method to alleviate the pressure. Firstly, all destination nodes of each source node on NoC are divided into several clusters. Secondly, multicast paths in the clusters are created based on the Hamiltonian path algorithm. The proposed routing can reduce the length of path and balance the communication load of each router. Lastly, we design a lightweight microarchitecture of NoC, which involves a customized multicast packet and a routing function. We use six datasets to verify the proposed multicast routing. Compared with unicast routing, the running time of path-based multicast routing achieves 5.1x speedup, and the number of hops and the maximum transmission latency of path-based multicast routing are reduced by 68.9% and 77.4%, respectively. The maximum length of path is reduced by 68.3% and 67.2% compared with the dual-path (DP) and multi-path (MP) multicast routing, respectively. Therefore, the proposed multicast routing has improved performance in terms of average latency and throughput compared with the DP or MP multicast routing.