Efficient SNN multi-cores MAC array acceleration on SpiNNaker 2

• Published in: Frontiers in neuroscience Vol. 17; p. 1223262
• Main Authors: Huang, Jiaxin, Kelber, Florian, Vogginger, Bernhard, Liu, Chen, Kreutz, Felix, Gerhards, Pascal, Scholz, Daniel, Knobloch, Klaus, Mayr, Christian G.
• Format: Journal Article
• Language: English
• Published: Lausanne Frontiers Research Foundation 07.08.2023; Frontiers Media S.A
• Subjects: Algorithms; Firing pattern; MAC array; multi-core load balancing deployment; Nervous system; Neural networks; Neurons; Neuroscience; Optimization algorithms; SNN; Software; Sparsity; SpGEMM; SpiNNaker 2; Temporal lobe
• ISSN: 1662-453X; 1662-4548; 1662-453X
• DOI: 10.3389/fnins.2023.1223262

The potential low-energy feature of the spiking neural network (SNN) engages the attention of the AI community. Only CPU-involved SNN processing inevitably results in an inherently long temporal span in the cases of large models and massive datasets. This study introduces the MAC array, a parallel architecture on each processing element (PE) of SpiNNaker 2, into the computational process of SNN inference. Based on the work of single-core optimization algorithms, we investigate the parallel acceleration algorithms for collaborating with multi-core MAC arrays. The proposed Echelon Reorder model information densification algorithm, along with the adapted multi-core two-stage splitting and authorization deployment strategies, achieves efficient spatio-temporal load balancing and optimization performance. We evaluate the performance by benchmarking a wide range of constructed SNN models to research on the influence degree of different factors. We also benchmark with two actual SNN models (the gesture recognition model of the real-world application and balanced random cortex-like network from neuroscience) on the neuromorphic multi-core hardware SpiNNaker 2. The echelon optimization algorithm with mixed processors realizes 74.28% and 85.78% memory footprint of the original MAC calculation on these two models, respectively. The execution time of echelon algorithms using only MAC or mixed processors accounts for ≤ 24.56% of the serial ARM baseline. Accelerating SNN inference with algorithms in this study is essentially the general sparse matrix-matrix multiplication (SpGEMM) problem. This article explicitly expands the application field of the SpGEMM issue to SNN, developing novel SpGEMM optimization algorithms fitting the SNN feature and MAC array.