ConvertNet: Training-time Model Scaling for In-vehicle Continuous Learning
Qinglong Zhang (Beijing Institute of Technology)
Rui Han (Beijing Institute of Technology)
Chi Harold Liu (Beijing Institute of Technology)
Guoren Wang (Beijing Institute of Technology)
Lydia Y. Chen (TU Delft - Electrical Engineering, Mathematics and Computer Science)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
Vehicles are transforming from transportation means to mobile intelligent terminals, powered by artificial intelligence (AI) technologies such as large language (LLM) and vision large models (VLM). To maintain high learning accuracy in all situations, in-vehicle AI models encounter the acute need to continuously learn and dynamically retrain AI models on the fly. The volatile resource demands of such stochastic retraining jobs and inference jobs of higher priority however can not be accommodated simultaneously by existing training systems, which rely on pre-generated compressed models of fixed architecture. In this paper, we propose ConvertNet, a novel continuous learning system that effectively scales up/down a compressed model through training-time neuron memorizer (TNM) - a max heap of neurons in tree structure. Based on TNM, ConvertNet estimates the model's resource-accuracy trade-off per neuron, thus efficiently utilizing the limited available resources to scale up and (re)train the model to improve its accuracy. Evaluated on six representative in-vehicle scenarios, comparative experiments against eleven state-of-the-art techniques show that ConvertNet achieves as much as 22.33% improvement in learning accuracy, and reduces energy consumption by 3.65x.