# Beacon: Post-Training Quantization with Integrated Grid Selection

## TL;DR

Beacon introduces a straightforward, tuning-free post-training quantization method that automatically determines optimal scaling factors, enabling efficient and competitive model compression without extensive calibration or heuristic tuning.

## Contribution

It presents a novel algorithm for per-channel PTQ that eliminates manual tuning by leveraging the geometry of scalar quantization, simplifying the quantization process.

## Key findings

- Achieves competitive accuracy with state-of-the-art methods
- Does not require back-propagation or large calibration sets
- Simplifies post-training quantization process

## Abstract

Quantization is a widely used compression technique for reducing the memory and computation costs of large pre-trained models. A key challenge in per-channel post-training quantization (PTQ) is selecting appropriate scaling factors to replace weight values with values from a scaled integer grid. Existing methods typically fix the scale at the outset via heuristic tuning or grid search. We propose Beacon, a simple and effective algorithm that eliminates the need for such manual tuning. Beacon performs per-channel PTQ directly using an unscaled grid and automatically determines the optimal scaling factors by exploiting the geometry of scalar quantization. It does not rely on back-propagation or large calibration sets. Despite its simplicity and tuning-free nature, Beacon achieves competitive performance compared to state-of-the-art methods, making it a practical solution for efficient model deployment.