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arXiv Machine Learning By Muhammad Rehan, Haider Ali, Muhammad Ali Munir, Moaz Amjad

FORGE: Forward-Only Test-Time Adaptation for Integer-Only Vision Models on Microcontrollers

Read the original on arXiv Machine Learning →

FORGE is a forward‑only test‑time adaptation technique designed for integer‑only vision models running on microcontrollers. It restores batch‑normalization statistics after BN folding by re‑normalizing each convolution’s per‑channel output using only forward‑pass estimates, enabling adaptation on deployed, folded integer models. The method achieves accuracy gains comparable to gradient‑based TENT, requires adapting only a few layers, works with single‑sample streaming, and has been validated on an ESP32‑S3 with minimal energy and latency overhead.

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 Machine Learning.

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