AUTHOR=Huang Jiaxin , Kelber Florian , Vogginger Bernhard , Liu Chen , Kreutz Felix , Gerhards Pascal , Scholz Daniel , Knobloch Klaus , Mayr Christian G. 

TITLE=Efficient SNN multi-cores MAC array acceleration on SpiNNaker 2

JOURNAL=Frontiers in Neuroscience

VOLUME=Volume 17 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2023.1223262

DOI=10.3389/fnins.2023.1223262

ISSN=1662-453X

ABSTRACT=The potential low-energy feature of the spiking neural network (SNN) engages the attention of the AI community. While only CPU-involved SNN processing inevitably results in an inherently long temporal span in the cases of large models and massive datasets. This paper 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 multicore 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 paper 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.