# Design Environment of Quantization-Aware Edge AI Hardware for Few-Shot Learning

arXiv: https://arxiv.org/abs/2602.12295 · HTML: https://mlsanity.com/paper/2602.12295/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 10 references that are in the map.
- First posted on arXiv 1 Feb 2026.

## Builds on (most cited first, 2 of 2)

- [A Survey of Quantization Methods for Efficient Neural Network Inference](https://mlsanity.com/paper/2103.13630/) (2021)
- [EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients](https://mlsanity.com/paper/2201.09699/) (2022)

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

- [Efficient Few-Shot Learning for Edge AI via Knowledge Distillation on MobileViT](https://mlsanity.com/paper/2603.26145/) (2026)

## Abstract (arXiv)

This study aims to ensure consistency in accuracy throughout the entire design flow in the implementation of edge AI hardware for few-shot learning, by implementing fixed-point data processing in the pre-training and evaluation phases. Specifically, the quantization module, called Brevitas, is applied to implement fixed-point data processing, which allows for arbitrary specification of the bit widths for the integer and fractional parts. Two methods of fixed-point data quantization, quantization-aware training (QAT) and post-training quantization (PTQ), are utilized in Brevitas. With Tensil, which is used in the current design flow, the bit widths of the integer and fractional parts need to be 8 bits each or 16 bits each when implemented in hardware, but performance validation has shown that accuracy comparable to floating-point operations can be maintained even with 6 bits or 5 bits each, indicating potential for further reduction in computational resources. These results clearly contribute to the creation of a versatile design and evaluation environment for edge AI hardware for few-shot learning.