C2-LSM: A Storm-NoC Based Neuromorphic Processor for High-Accuracy Liquid State Machine With Cube-Cluster Topology

The liquid state machine (LSM), a reservoir computing variant of spiking neural networks (SNNs), has been widely adopted for its low training complexity. In this work, we propose C2-LSM, a neuromorphic processor designed through algorithm-hardware co-design to achieve high accuracy across diverse tasks. At the algorithm level, inspired by the "small-world" structure of the biological brain, we introduce a novel reservoir layer in which neurons are interconnected using a cube-cluster topology. For hardware implementation, the customized C2-LSM processor supports runtime configurability of reservoir size and connection sparsity, enabling high classification accuracy across a range of spatiotemporal tasks. Additionally, a Network-on-Chip (NoC) with a Storm routing algorithm is developed to improve the spike event transmission throughput among reservoir neurons. C2-LSM is implemented on an AMD Virtex UltraScale+ VCU129 FPGA running at 250 MHz. With on-chip learning, it achieves accuracies of 98.02%, 94.26%, and 93.00% on MNIST, N-MNIST, and FSDD datasets, respectively, outperforming recently benchmarked LSM neuromorphic processors across all three tasks. For the MNIST task, it achieves an inference speed of 1155 FPS and a learning speed of 1154 FPS, along with a high power efficiency of 103 GSOPS/W.