Integer AI inference significantly reduces computational complexity in embedded systems. Quantization-aware training (QAT) helps mitigate accuracy degradation associated with post-training quantization but still overlooks the impact of integer rescaling during inference, which is a hardware costly operation in integer-only AI inference. This work shows that rescaling cost can be dramatically reduced post-training, by applying a stronger quantization to the rescale multiplicands at no model-quality loss. Furthermore, we introduce Rescale-Aware Training, a fine tuning method for ultra-low bit-width rescaling multiplicands. Experiments show that even with 8x reduced rescaler widths, the full accuracy is preserved through minimal incremental retraining. This enables more energy-efficient and cost-efficient AI inference for resource-constrained embedded systems.
@article{arxiv.2510.11484,
title = {Rescaling-Aware Training for Efficient Deployment of Deep Learning Models on Full-Integer Hardware},
author = {Lion Mueller and Alberto Garcia-Ortiz and Ardalan Najafi and Adam Fuks and Lennart Bamberg},
journal= {arXiv preprint arXiv:2510.11484},
year = {2025}
}