5 papers
CMAX-CAMEL: A Coarse-to-Fine Adaptive, Memory-Efficient, and Low-Power Edge Processor for Contrast Maximization
Kyeongpil Min, Jongin Choi, Kyeongwon Lee +1
Contrast maximization (CMAX) is a direct geometric framework for event-based motion estimation, but its iterative warp-and-accumulate pipeline incurs input-dependent computation an…
SA-Kura: An Energy-Efficient Systolic Array Accelerator for Locally-Coupled Kuramoto Drift in Diffusion Sampling
Jeongmin Jin, Kyeongwon Lee, Mundo Jeong +2
Diffusion inference remains costly for edge deployment, yet existing accelerators focus almost exclusively on score networks because standard drift is merely a trivial linear scali…
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…
Asymptotic Error Bounds and Fractional-Bit Design for Fixed-Point Grover's Quantum Algorithm Emulation
Seonghyun Choi, Kyeongwon Lee, Jongin Choi +1
Quantum computing (QC) emulators, which simulate quantum algorithms on classical hardware, are indispensable platforms for testing quantum algorithms before scalable quantum comput…