CPP-SGS: Cycle-Accurate Power Prediction Framework via SNN and Genetic Signal Selection

Effective power management is crucial for optimizing the performance and longevity of integrated circuits. Cycle-accurate power prediction can help power management during runtime. This paper introduces a Cycle-accurate Power Prediction framework via Spiking neural networks (SNNs) and Genetic signal Selection (CPP-SGS), which integrates SNNs and Genetic Algorithms (GAs) to predict real-time power consumption of chips. We apply GAs to select the most relevant signals as the input to SNNs to reduce the model size and inference time, making it well-suited for dynamic power estimation in real-time scenarios. The experimental results show that CCP-SGS outperforms the state-of-the-art approaches, with a normalized root mean squared error (NRMSE) of less than 1.6%.