AI RESEARCH
When Spike Sparsity Does Not Translate to Deployed Cost: VS-WNO on Jetson Orin Nano
arXiv CS.LG
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ArXi:2604.17040v1 Announce Type: new Spiking neural operators are appealing for neuromorphic edge computing because event-driven substrates can, in principle, translate sparse activity into lower latency and energy. Whether that advantage survives deployment on commodity edge-GPU software stacks, however, remains unclear. We study this question on a Jetson Orin Nano 8 GB using five pretrained variable-spiking wavelet neural operator (VS-WNO) checkpoints and five matched dense wavelet neural operator (WNO) checkpoints on the Darcy rectangular benchmark.