5 citations · 20 across the 34 of their papers we have counts for
9 papers · 1 filter
QUILL: An Algorithm-Architecture Co-Design for Cache-Local Deformable Attention
Hyunwoo Oh, Hanning Chen, Sanggeon Yun +5
Deformable transformers deliver state-of-the-art detection but map poorly to hardware due to irregular memory access and low arithmetic intensity. We introduce QUILL, a schedule-aw…
T-SAR: A Full-Stack Co-design for CPU-Only Ternary LLM Inference via In-Place SIMD ALU Reorganization
Hyunwoo Oh, KyungIn Nam, Rajat Bhattacharjya +7
Recent advances in LLMs have outpaced the computational and memory capacities of edge platforms that primarily employ CPUs, thereby challenging efficient and scalable deployment. W…
Draft and Refine with Visual Experts
Sungheon Jeong, Ryozo Masukawa, Jihong Park +5
While recent Large Vision-Language Models (LVLMs) exhibit strong multimodal reasoning abilities, they often produce ungrounded or hallucinated responses because they rely too heavi…
LogHD: Robust Compression of Hyperdimensional Classifiers via Logarithmic Class-Axis Reduction
Sanggeon Yun, Hyunwoo Oh, Ryozo Masukawa +3
Hyperdimensional computing (HDC) suits memory, energy, and reliability-constrained systems, yet the standard "one prototype per class" design requires memory (with clas…
DecoHD: Decomposed Hyperdimensional Classification under Extreme Memory Budgets
Sanggeon Yun, Hyunwoo Oh, Ryozo Masukawa +1
Decomposition is a proven way to shrink deep networks without changing input-output dimensionality or interface semantics. We bring this idea to hyperdimensional computing (HDC), w…
MissionHD: Hyperdimensional Refinement of Distribution-Deficient Reasoning Graphs for Video Anomaly Detection
Sanggeon Yun, Raheeb Hassan, Ryozo Masukawa +2
LLM-generated reasoning graphs, referred to as mission-specific graphs (MSGs), are increasingly used for video anomaly detection (VAD) and recognition (VAR). However, they are typi…
A Few Large Shifts: Layer-Inconsistency Based Minimal Overhead Adversarial Example Detection
Sanggeon Yun, Ryozo Masukawa, Hyunwoo Oh +2
Deep neural networks (DNNs) are highly susceptible to adversarial examples---small, malicious perturbations that can cause incorrect predictions. We introduce a lightweight, plug-i…
PacketCLIP: Multi-Modal Embedding of Network Traffic and Language for Cybersecurity Reasoning
Ryozo Masukawa, Sanggeon Yun, Sungheon Jeong +5
Traffic classification is vital for cybersecurity, yet encrypted traffic poses significant challenges. We present PacketCLIP, a multi-modal framework combining packet data with nat…
Hyperdimensional Intelligent Sensing for Efficient Real-Time Audio Processing on Extreme Edge
Sanggeon Yun, Ryozo Masukawa, Hanning Chen +6
The escalating challenges of managing vast sensor-generated data, particularly in audio applications, necessitate innovative solutions. Current systems face significant computation…