Author

Hyeran Jeon

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Open access Aug 2026

Uncovering Power and Frequency Behaviors and Their Security Implications in Edge GPU Platforms

Edge platforms increasingly rely on GPUs and deep learning accelerators (DLAs) to support real-time computing of emerging workloads. However, their power, frequency, and security characteristics remain largely unexplored. This paper provides the first in-depth characterization of power and frequency behaviors of NVIDIA...

M. Rafi, Kevin Chau, Hyeran Jeon · ACM Transactions on Architec... · 0 citations

#machine learning Preprint Sep 2026

Accelerating the Mitigation of LLM Inference Nondeterminism Across GPU Architectures

Large language model (LLM) outputs are expected to be reproducible under greedy decoding, yet in practice the same model, prompt, and software stack produce different outputs on different GPUs. The root cause is floating-point non-associativity combined with hardware-dependent kernel selection. Inference frameworks sel...

L. Cooper, Shinnung Jeong, Hyeran Jeon et al. · 0 citations

Preprint Sep 2026

AutoUVM: Automated Prefetching Framework for LLMs under UVM Oversubscription

AutoUVM is proposed, an automated, framework-aware UVM prefetching system for efficient LLM execution under memory oversubscription and bridges the semantic gap between deep learning frameworks and UVM by exposing tensor-level access information and enabling policy-driven prefetching at fine granularity.

Mao Lin, Hui Feng, Xian-Zhong Ding et al. · 0 citations