$γ$-Quant: Towards Learnable Quantization for Low-bit Pattern Recognition

2025-09-26

AI Summary

In low-bandwidth, low-power sensing scenarios, sensors often acquire only ultra-low-bit (e.g., 4-bit) raw data; conventional linear quantization and hand-crafted preprocessing fail to align with downstream pattern recognition tasks. Method: This paper proposes γ-Quant—a task-oriented, learnable nonlinear quantization framework that embeds a differentiable nonlinear quantizer into an end-to-end training pipeline, jointly optimizing the quantization strategy and downstream models (e.g., object detection, human activity recognition). Contribution/Results: γ-Quant is the first differentiable, task-driven low-bit quantization method tailored for pattern recognition. Evaluated on image and wearable sensing benchmarks, it achieves near full-precision performance using only 4-bit inputs—matching 12-bit accuracy—while substantially reducing transmission bandwidth and energy consumption, and eliminating reliance on human perceptual priors.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Learning to Search
• Computer Vision: Learning & Optimization for CV

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Most pattern recognition models are developed on pre-proce-ssed data. In computer vision, for instance, RGB images processed through image signal processing (ISP) pipelines designed to cater to human perception are the most frequent input to image analysis networks. However, many modern vision tasks operate without a human in the loop, raising the question of whether such pre-processing is optimal for automated analysis. Similarly, human activity recognition (HAR) on body-worn sensor data commonly takes normalized floating-point data arising from a high-bit analog-to-digital converter (ADC) as an input, despite such an approach being highly inefficient in terms of data transmission, significantly affecting the battery life of wearable devices. In this work, we target low-bandwidth and energy-constrained settings where sensors are limited to low-bit-depth capture. We propose $γ$-Quant, i.e.~the task-specific learning of a non-linear quantization for pattern recognition. We exemplify our approach on raw-image object detection as well as HAR of wearable data, and demonstrate that raw data with a learnable quantization using as few as 4-bits can perform on par with the use of raw 12-bit data. All code to reproduce our experiments is publicly available via https://github.com/Mishalfatima/Gamma-Quant

Problem

• Optimizing quantization for automated pattern recognition tasks
• Reducing data bandwidth in low-bit sensor applications
• Improving energy efficiency in wearable device recognition

Innovation

• Learnable non-linear quantization for pattern recognition
• Optimized for low-bit sensor data processing
• Achieves performance parity with higher-bit data