TrainDeeploy: Hardware-Accelerated Parameter-Efficient Fine-Tuning of Small Transformer Models at the Extreme Edge

On-device tuning of deep neural networks enables long-term adaptation at the edge, while keeping data fully private and secure. However, the high computational demand of back-propagation remains a challenge for ultra-low-power, memory-constrained extreme-edge devices. Attention-based models further exacerbate this challenge, given their complex architecture and scale. We present TrainDeeploy, a novel framework that unifies efficient inference with on-device training on heterogeneous ultra-low-power System-on-Chips (SoCs). TrainDeeploy is the first complete on-device training pipeline for extreme edge SoCs supporting both Convolutional Neural Networks (CNNs) and Transformer models, as well as multiple training techniques, such as selective layer-wise fine-tuning and Low-Rank Adaptation (LoRA). On a RISC-V-based heterogeneous SoC, we demonstrate the first end-to-end fine-tuning of a complete Transformer, Compact Convolutional Transformer (CCT), achieving 11 trained images per second. We show that LoRA on-device leads to a 23% reduction in dynamic memory usage, 15× reduction in trainable parameters and gradients, and 1.6× reduction in memory transfer compared to full backpropagation. TrainDeeploy achieves up to 4.6 FLOP/cycle on CCT (0.28M Param, 71–126M FLOPs) and leading-edge performance up to 13.4 FLOP/cycle on Deep-AE (0.27M Param, 0.8M FLOPs), while simultaneously widening the scope compared to state-of-the-art frameworks to support both CNNs and Transformers with parameter-efficient tuning.