Error Amplification Blocks ANN-to-SNN Conversion for Continuous Control
Small approximation errors compound over time, wrecking SNN performance in RL tasks.
Researchers from multiple institutions pinpointed why ANN-to-SNN conversion underperforms in continuous control scenarios: error amplification. When converting a trained artificial neural network into a spiking neural network for reinforcement learning, small action approximation errors become temporally correlated across decision steps. This leads to a cumulative state distribution shift and severe performance degradation, a problem largely overlooked in prior conversion work that focused on classification tasks.
To solve this, the team introduced Cross-Step Residual Potential Initialization (CRPI), a gradient-free mechanism that preserves residual membrane potentials between decision steps. This prevents correlated errors from building up. Experiments on continuous control benchmarks with vector and visual observations showed CRPI integrates cleanly into existing conversion pipelines and significantly recovers lost performance. The paper has been accepted at ICML2026, highlighting continuous control as a critical challenge for efficient SNN deployment.
- Small action approximation errors in ANN-to-SNN conversion become temporally correlated, causing state distribution shift in continuous control.
- Proposed CRPI (Cross-Step Residual Potential Initialization) is a lightweight, gradient-free method that suppresses correlated errors by carrying over membrane potentials.
- Experiments on continuous control benchmarks with vector/visual observations show CRPI recovers lost performance and integrates into existing pipelines.
Why It Matters
Enables efficient, low-power SNN deployment in robotics and real-time control without costly retraining.