Event-Driven Neuromorphic Computing & SNN Dynamics
Enterprise Autonomous Agent Spiking Neural Network (SNN) LIF Neuromorphic Simulator
Benchmark event-driven Spiking Neural Networks (SNN) utilizing Leaky Integrate-and-Fire (LIF) and Adaptive-LIF (ALIF) membrane dynamics. Simulate temporal spike encoding, Address-Event Representation (AER), synaptic sparsity, and femtojoule-per-synaptic-op energy scaling against dense GPU baselines.
Neuromorphic Event-Driven SNN Performance Matrix
- Event-Driven Sparsity: Achieves >85% idle compute suppression by replacing dense continuous floating-point matrix multiplications with event-driven synaptic spike additions ($V \leftarrow V + W$).
- Synaptic Energy Efficiency: Consumes sub-15 picojoules per synaptic operation (pJ/SOP), delivering a 30x to 50x power reduction over dense GPU tensor inference.
- Sub-Millisecond Temporal Decisions: Temporal Address-Event Representation (AER) and Time-to-First-Spike (TTFS) protocol resolve agent routing decisions in under 5 milliseconds without batching latency.