Efficient AI Model Deployment Using Quantization Analysis Tool

A new arXiv paper (2609.11954v1) presents Quantization Analysis Tool, a system built on the ONNX framework that streamlines quantization workflows for deploying deep learning models on resource-constrained edge and low-power devices. The tool offers layer-wise sensitivity analysis and visualization of weight and activation distributions to guide precision selection, and experimental evaluations across multiple neural network architectures show it improves quantized accuracy. The authors say the tool helps developers trade off model size, latency, and accuracy in real-world deployment scenarios.

arXiv:2609.11954v1 Announce Type: new Abstract: As deep learning models are increasingly deployed on resource constrained devices, the demand for efficient model optimization techniques continues to grow. Effective deployment of AI models on edge and low power platforms requires optimization methods that reduce model size and computational cost while maintaining high accuracy. This paper presents Quantization Analysis Tool, a practical system designed to streamline quantization workflows and support performance efficient model deployment. Built on the ONNX framework for broad interoperability, the tool provides detailed layer-wise sensitivity analysis, visualization of weight and activation distributions, and insights to guide precision selection. By identifying layers that are resilient or sensitive to reduced precision, the tool enables developers to make informed trade-offs between model size, latency, and accuracy. Experimental evaluations across multiple neural network architectures demonstrate that the tool effectively improves the quantized accuracy, leading to improved efficiency in real-world deployment scenarios. The tool also provides developers valuable insights into the effects on quantization on the model and its accuracy. This work highlights the tools capabilities, practical applications, and its role in enabling efficient AI model deployment through robust quantization analysis