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Neuromorphic vs Clocked Processing: Energy-per-Spike Simulator

Two live 8x8 processing arrays fed the same sparse sensor-event stream — one event-driven (spiking, fires only above a threshold), one clocked (samples every node on every tick regardless of activity). Watch the energy meters diverge and see why neuromorphic edge hardware saves power.

AI & Machine Learning3DModerate60 FPS📱 Mobile-adapted⇄ 2D version

Neuromorphic edge chips claim huge energy savings over conventional digital signal processing because they only spend power when a neuron actually fires. This simulator puts that claim on the bench: two live 8×8 arrays of processing nodes are fed the identical sparse sensor-event stream, one array integrating events into a membrane potential and spiking only past a threshold, the other sampling every node on every clock tick regardless of activity. Live energy meters accumulate for each array in real time, and a ratio readout shows exactly how much the clocked array's fixed per-cycle cost outpaces the event-driven array's activity-proportional cost as you dial event sparsity, spike threshold and clock rate.

⚙ Under the hood

Two live 8x8 processing arrays are fed the same sparse sensor-event stream — one fires only above a spike threshold, the other samples every node on every clock tick — with real-time energy meters showing why event-driven neuromorphic hardware saves power at the edge.

neuromorphicedge computingspiking neural networkenergy efficiencyevent-drivensensors

3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install