TASTE: Throughput-Aware Batch Size Tuning for On-Device Edge Learning

2026-09-07

AI Summary

本文提出了一种基于贝叶斯优化的批量大小调整方法，以提高边缘设备上深度学习模型训练的吞吐量，同时保持预测精度。

Abstract

The rise of privacy-preserving artificial intelligence (AI) has shifted the focus of model adaptation and personalization towards on-device learning, where deep learning models are finetuned directly on edge hardware using local user data. However, this shift requires optimization of deep learning training on resource-constrained hardware to maximize throughput while maintaining predictive accuracy. This paper introduces a novel technique for on-device model training that incorporates an efficient Bayesian optimization-based batch size tuning approach to maximize hardware throughput. To evaluate the impact of this hyperparameter on the learning dynamics, we investigated two distinct paradigms: standard supervised learning (SL) and online continual learning (CL). Experimental results across various edge devices demonstrate a throughput ceiling, beyond which increasing the batch size yields no additional throughput gains. The proposed tuning approach identifies the optimal batch size, which, when combined with gradient accumulation and linear learning rate scaling, achieves up to a 2X increase in training throughput on platforms such as Raspberry Pi 4 compared to maximum batch sizes, without compromising model accuracy. Furthermore, in the CL paradigm, we demonstrate that optimal batch sizes maintain the stability-plasticity balance required for incremental learning, effectively mitigating catastrophic forgetting while maximizing computational efficiency on edge-hardware.

Problem

• on-device learning
• throughput optimization
• resource-constrained hardware
• batch size tuning
• predictive accuracy

Innovation

• Bayesian optimization
• batch size tuning
• on-device learning
• throughput maximization
• continual learning