# Rescaling-Aware Training for Efficient Deployment of Deep Learning Models on Full-Integer Hardware

arXiv: https://arxiv.org/abs/2510.11484 · HTML: https://mlsanity.com/paper/2510.11484/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 11 references that are in the map.
- First posted on arXiv 13 Oct 2025.

## Builds on (most cited first, 11 of 11)

- [Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation](https://mlsanity.com/paper/1308.3432/) (2013)
- [Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](https://mlsanity.com/paper/1712.05877/) (2017)
- [Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations](https://mlsanity.com/paper/1609.07061/) (2016)
- [Data-Free Quantization Through Weight Equalization and Bias Correction](https://mlsanity.com/paper/1906.04721/) (2019)
- [Additive Powers-of-Two Quantization: An Efficient Non-uniform Discretization for Neural Networks](https://mlsanity.com/paper/1909.13144/) (2019)
- [Trained Quantization Thresholds for Accurate and Efficient Fixed-Point Inference of Deep Neural Networks](https://mlsanity.com/paper/1903.08066/) (2019)
- [DeepShift: Towards Multiplication-Less Neural Networks](https://mlsanity.com/paper/1905.13298/) (2019)
- [Fighting Quantization Bias With Bias](https://mlsanity.com/paper/1906.03193/) (2019)
- [Power-of-Two Quantization for Low Bitwidth and Hardware Compliant Neural Networks](https://mlsanity.com/paper/2203.05025/) (2022)
- [DenseShift: Towards Accurate and Efficient Low-Bit Power-of-Two Quantization](https://mlsanity.com/paper/2208.09708/) (2022)
- [eIQ Neutron: Redefining Edge-AI Inference with Integrated NPU and Compiler Innovations](https://mlsanity.com/paper/2509.14388/) (2025)

## Built on by (most cited first, 1 of 1)

- [A Symbolic Neural CPU for Quantization-Simulated Writeback and Interpretable Program Execution](https://mlsanity.com/paper/2607.10021/) (2026)

## Abstract (arXiv)

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.