Enhanced Vector Quantization for Embedded Machine Learning: A Post-Training Approach With Incremental Clustering

Thommas Flores

doi:10.1109/ACCESS.2025.3532849

Enhanced Vector Quantization for Embedded Machine Learning: A Post-Training Approach With Incremental Clustering

2025-01-01

0

OA

AI

DOI：10.1109/ACCESS.2025.3532849

Original

Original request for help

Save

Abstract

En 中文

TinyML enables the deployment of Machine Learning (ML) models on resource-constrained devices, addressing a growing need for efficient, low-power AI solutions. However, significant challenges remain due to strict memory, processing, and energy limitations. This study introduces a novel method to optimize Post-Training Quantization (PTQ), a widely used technique for reducing model size, by integrating Vector Quantization (VQ) with incremental clustering. While VQ is a technique that reduces model size by grouping similar parameters, incremental clustering, implemented via the AutoCloud K-Fixed algorithm, preserves accuracy during compression. This combined approach was validated on an automotive dataset predicting CO2 emissions from vehicle sensor measurements such as mass air flow, intake pressure, temperature, and speed. The model was quantized and deployed on Macchina A0 hardware, demonstrating over 90% compression with negligible accuracy loss. Results show improved performance and deployment efficiency, showcasing the potential of this combined technique for real-world embedded applications.

Keywords:

Clustering algorithms

Computational modeling

Accuracy

Adaptation models

Training

Embedded systems

Computational efficiency

Automotive engineering

Vector quantization

Data models

post-training quantization

vector quantization

automotive sensors

pollution

Enhanced Vector Quantization for Embedded Machine Learning: A Post-Training Approach With Incremental Clustering

Abstract

Journal

Organization