Characterizing Neuromorphic Workloads from A System Perspective

Neuromorphic computing is emerging as a cornerstone for next-generation intelligent systems. The lack of standardized benchmarks hinders fair comparison across diverse neuromorphic solutions and fragments research efforts. Here, we introduce a systematical workload characterization methodology that enables quantitative selection of representative benchmarks. Our approach encodes each spiking neural network (SNN) into a unified feature vector by extracting spatial and temporal features from its network topology and spike rasters. Evaluated on 1,030 models across four hardware platforms, the extracted features achieve over 90% accuracy in performance regression and classification, demonstrating both completeness in capturing SNN performance metrics and hardware independence. We expect that this work will provide a principled and scalable foundation for benchmarking neuromorphic systems.