FORGE: Forward-Only Test-Time Adaptation for Integer-Only Vision Models on Microcontrollers
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Computer Science > Computer Vision and Pattern Recognition
arXiv:2609.01683 (cs)
[Submitted on 1 Sep 2026]
Title:FORGE: Forward-Only Test-Time Adaptation for Integer-Only Vision Models on Microcontrollers
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Abstract:Vision models deployed on microcontrollers (MCUs) are quantized to integer-only arithmetic and run in inference-only runtimes that do not carry the machinery backpropagation needs: the standard tool for adapting a model to the distribution shift (sensor noise, blur, lighting) it meets in the field. Existing forward-only test-time adaptation (TTA) methods either run only on server- or edge-GPU-class models (not true microcontroller integer execution), or require the batch-normalization (BN) layers that integer deployment fuses away. We present a forward-only TTA method that operates on deployed, BN-folded, integer-only convolutional networks. The key observation is that fusing BN into the preceding convolution, a mandatory step for integer inference, destroys the statistics that normalization-based adaptation relies on. We restore adaptation by re-normalizing each folded convolution's per-channel output to its clean training statistics, using only forward-pass estimates. The method (i) recovers most of gradient-based TENT's accuracy gain (+20.9 vs. +24.9 points) and matches forward-only BN adaptation, while being the only method that runs on a folded integer-only model; (ii) needs to adapt only 3 of 21 layers (selected without seeing the test corruptions) to recover 93% of the benefit; (iii) survives single-sample streaming with a batch-size-scaled momentum; and (iv) generalizes across three datasets (up to 200 classes) and two architectures. We validate bit-exact int8 convolution execution and deploy on an ESP32-S3, where, measured with a Nordic PPK2 power profiler, the forward-only adaptation (a lightweight fp32 recalibration around the int8 convolutions) costs only 8.3 mJ (6.8% of inference energy) and 21.9 ms on the deployed SIMD-optimized model: forward-only adaptation is cheap on a real microcontroller.
| Comments: | 16 pages, 5 figures, 9 tables. Published in Transactions on Machine Learning Research (2026). OpenReview: this https URL. Code and checkpoints: this https URL |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Hardware Architecture (cs.AR); Machine Learning (cs.LG) |
| ACM classes: | I.2.6; I.5.4; C.3 |
| Cite as: | arXiv:2609.01683 [cs.CV] |
| (or arXiv:2609.01683v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2609.01683
arXiv-issued DOI via DataCite (pending registration)
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| Journal reference: | Transactions on Machine Learning Research, 2026 |
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