Overview
- The preprint benchmarks multiple designs and reports surrogate‑gradient SNNs within 1–2% of ANN accuracy, converging by about 20 epochs with latency around 10 milliseconds.
- ANN‑to‑SNN converted models achieve competitive accuracy but require higher spike counts and longer simulation windows, increasing energy use and response time.
- STDP‑trained networks converge more slowly yet record the fewest spikes and energy as low as roughly 5 millijoules per inference, favoring unsupervised and ultra‑low‑power workloads.
- The authors identify strong fit for robotics, event‑driven vision, and edge AI, especially when paired with neuromorphic hardware.
- This evidence comes from a non‑peer‑reviewed arXiv study, and commentary highlights remaining hurdles in scalable training, reliable ANN‑to‑SNN conversion, and hardware standardization.