Exploring Stimulus-Response Dynamics in Neuromorphic and Classical Hardware: How Inference Timing Affects Results

Spiking Neural Networks and Dynamic Vision Sensors offer a biologically inspired approach to computation and perception, with information processed only when relevant events occur. Unlike conventional cameras that capture frames at fixed intervals, event-driven visual sensors allow the timing of inference to be an adaptable design choice. This work examines how controlling when to respond affects accuracy, latency, and energy consumption, using a simple Rock, Scissor, Paper gesture recognition task. Across classical and neuromorphic hardware, we show that integrating events over an optimal temporal window allows the system to act efficiently, avoiding premature or unnecessary reactions. On neuromorphic platforms, this approach achieves high accuracy while consuming only micro-joules per inference, far below the energy demands of conventional hardware. These findings suggest that selectively triggering responses based on accumulated sensory evidence, similar to strategies evolved in biological organisms, can provide both speed and efficiency, highlighting how deciding when to act is as important as how to act.