ANGraph: A GNN-Based Performance Prediction Framework for Asynchronous Neuromorphic Hardware

Design space exploration (DSE) through system-level simulation is essential for designing energy-efficient asynchronous neuromorphic hardware, which is increasingly promising in edge AI applications. However, there are significant mismatches between system-level predictions and gate-level simulations, resulting in low precision when predicting performance during the DSE process for asynchronous neuromorphic hardware. To address this issue, we put forward ANGraph, a graph neural network (GNN)-based performance prediction framework for asynchronous neuromorphic hardware. In the ANGraph framework, we transform the intermediate representation of systemlevel simulations into graphs, collect gate-level circuit simulation results to build benchmarks with over one million samples, and train a GNN model to predict hardware latency for asynchronous neuromorphic hardware. Additionally, we use a residual network (ResNet)-based method to predict the power consumption of asynchronous neuromorphic hardware. We evaluate these two models on additional datasets without extra training across different scales, process nodes, and traffic patterns of input data. Compared to the latency predictions from the state-of-the-art simulator, we improve the R -square score by 0.69 and reduce root mean square error (RMSE) by 76% on average across all datasets. We also achieve an R-square score of 0.98 and a mean absolute percentage error (MAPE) of 0.88% for the power consumption prediction task. The benchmarks and models are available at https://github.com/HuaGuaiGuai/ANGraph.