5 papers
Energy- and Memory-Efficient PEFT Methods for Personalized On-Device SLMs on Consumer GPUs
Kuanysh Akhmetzhanov, Jurn-Gyu Park
Despite rapid advances in large language models (LLMs), deploying and personalizing them on resource-constrained devices remains impractical due to high VRAM, time, and energy cost…
Energy-Efficient GPU DVFS for Fine-Tuning of SLMs on Resource-constrained Embedded Devices
Jurn-Gyu Park, Sanzhar Zholdybayev, Aidar Amangeldi +1
Dynamic Voltage Frequency Scaling (DVFS) on resource-constrained embedded GPU platforms is essential for energy-efficient small language model (SLM) fine-tuning, as privacy- and pe…
Efficient PEFT Methods with Adaptive Checkpointing for Vision Models and VLMs on Resource Constrained Consumer-GPUs
Altay Toktassyn, Jurn-Gyu Park
Modern pretrained vision models achieve strong accuracy but demand substantial GPU memory for fine-tuning, making edge deployment impractical. This paper compares five parameter-ef…
Energy-Efficient Vision Transformer Inference for Edge-AI Deployment
Nursultan Amanzhol, Jurn-Gyu Park
The growing deployment of Vision Transformers (ViTs) on energy-constrained devices requires evaluation methods that go beyond accuracy alone. We present a two-stage pipeline for as…
Efficient-Husformer: Efficient Multimodal Transformer Hyperparameter Optimization for Stress and Cognitive Loads
Merey Orazaly, Fariza Temirkhanova, Jurn-Gyu Park
Transformer-based models have gained considerable attention in the field of physiological signal analysis. They leverage long-range dependencies and complex patterns in temporal si…