4 papers
LoRA-Edge: Tensor-Train-Assisted LoRA for Practical CNN Fine-Tuning on Edge Devices
Hyunseok Kwak, Kyeongwon Lee, Jae-Jin Lee +1
On-device fine-tuning of CNNs is essential to withstand domain shift in edge applications such as Human Activity Recognition (HAR), yet full fine-tuning is infeasible under strict…
FiCABU: A Fisher-Based, Context-Adaptive Machine Unlearning Processor for Edge AI
Eun-Su Cho, Jongin Choi, Jeongmin Jin +2
Machine unlearning, driven by privacy regulations and the "right to be forgotten", is increasingly needed at the edge, yet server-centric or retraining-heavy methods are impractica…
ASAP-FE: Energy-Efficient Feature Extraction Enabling Multi-Channel Keyword Spotting on Edge Processors
Jongin Choi, Jina Park, Woojoo Lee +2
Multi-channel keyword spotting (KWS) has become crucial for voice-based applications in edge environments. However, its substantial computational and energy requirements pose signi…
Standalone FPGA-Based QAOA Emulator for Weighted-MaxCut on Embedded Devices
Seonghyun Choi, Kyeongwon Lee, Jae-Jin Lee +1
Quantum computing QC emulation is crucial for advancing QC applications, especially given the scalability constraints of current devices. FPGA-based designs offer an efficient and…