arXiv AI October 28, 2026 By Arooba Farhan, Afsah Syed, Muhammad Fahd Ibrahim Bhatti, Muhammad Areeb Khalid Kidwai, Muhammad Ahsan Salar Khan, Vincenzo Romano, Muhammad Ali Siddiqi

Hardware-Matched Spiking Neural Networks for Real-Time Purkinje Cell Spike Classification

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The paper introduces two compact spiking neural networks for real‑time classification of Purkinje cell spikes on untethered headstages. ConvSNN is mapped to the Matrix Vector Processor of an EFM32PG28 microcontroller, achieving a CS F1 of 0.873 with 1,018 parameters and reducing latency by 62.3% while extending battery life to 33.73 hours. StreamSNN processes one waveform value per timestep on a dedicated SKY130 circuit, reaching a CS F1 of 0.837 with 527 parameters, occupying 0.173 mm² and consuming 0.46 µJ per event with 0.2 ms computation within an 8.3 ms end‑to‑end latency.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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