Owing to their low power consumption and high energy efficiency, neuromorphic processors have found extensive applications across various intelligent computing domains, including artificial intelligence (AI) and low-power edge computing. Homogeneous neuromorphic processors demonstrate a unique integration of general-purpose computing versatility and neuromorphic computing acceleration capabilities. These processors effectively address the intensive memory access demands inherent in neuromorphic computing through the implementation of tightly coupled local memory, specifically Scratchpad Memory (SPM). This paper presents the design of SPM-based memory access acceleration mechanisms, which have been specifically developed and optimized to accommodate the data characteristics and computational processes of typical Spiking Neural Networks (SNNs). The proposed scheme significantly enhances computational efficiency by reducing both memory access operations and computational instruction processing. Comparative performance evaluations reveal that the proposed design achieves a 28.99% speedup in Liquid State Machine operations and a 14.95% speedup in Spiking Convolutional Neural Network computations when benchmarked against a baseline homogeneous processor equipped with SPM.
y et al. (Mon,) studied this question.