arXiv subjects

Changhyun Kim

Publications and source records attributed to Changhyun Kim.

5 recordsLinked to original sources

arXiv2026

Estimating Subgraph Importance with Structural Prior Domain Knowledge

We propose a subgraph importance estimation method for pretrained Graph Neural Networks (GNNs) on graph-level tasks, formulated as a linear Group Lasso regression problem in the embedding space. Our method effectively leverages prior domain knowledge of graph substructures, while remaining independent of the specific form of the output layer or readout function used in the GNN architecture, and it does not require access to ground-truth target labels. Experiments on real-world graph datasets demonstrate that our method consistently outperforms existing baselines in subgraph importance estimation. Furthermore, we extend our method to identify important nodes within the graph.

cs.LG↗

arXiv2025

PIMphony: Overcoming Bandwidth and Capacity Inefficiency in PIM-based Long-Context LLM Inference System

The expansion of long-context Large Language Models (LLMs) creates significant memory system challenges. While Processing-in-Memory (PIM) is a promising accelerator, we identify that it suffers from critical inefficiencies when scaled to long contexts: severe channel underutilization, performance-limiting I/O bottlenecks, and massive memory waste from static KV cache management. In this work, we propose PIMphony, a PIM orchestrator that systematically resolves these issues with three co-designed techniques. First, Token-Centric PIM Partitioning (TCP) ensures high channel utilization regardless of batch size. Second, Dynamic PIM Command Scheduling (DCS) mitigates the I/O bottleneck by overlapping data movement and computation. Finally, a Dynamic PIM Access (DPA) controller enables dynamic memory management to eliminate static memory waste. Implemented via an MLIR-based compiler and evaluated on a cycle-accurate simulator, PIMphony significantly improves throughput for long-context LLM inference (up to 72B parameters and 1M context length). Our evaluations show performance boosts of up to 11.3x on PIM-only systems and 8.4x on xPU+PIM systems, enabling more efficient deployment of LLMs in real-world long-context applications.

cs.AR↗

arXiv2025

Integrated bright source of polarization-entangled photons using lithium niobate photonic chips

Quantum photonics has rapidly advanced as a key area for developing quantum technologies by harnessing photons' inherent quantum characteristics, particularly entanglement. Generation of entangled photon pairs, known as Bell states, is crucial for quantum communications, precision sensing, and quantum computing. While bulk quantum optical setups have provided foundational progress, integrated quantum photonic platforms now offer superior scalability, efficiency, and integrative potential. In this study, we demonstrate a compact and bright source of polarization-entangled Bell state utilizing continuous-wave pumping on thin film lithium niobate (TFLN) integrated photonics. Our periodically poled lithium niobate device achieves on-chip brightness of photon pair generation rate of 508.5 MHz/mW, surpassing other integrated platforms including silicon photonics. This demonstration marks the first realization of polarization entanglement on TFLN platforms. Experimentally measured metrics confirm high-quality entangled photon pairs with a purity of 0.901, a concurrence of 0.9, and a fidelity of 0.944. We expect our compact quantum devices to have great potential for advancing quantum communication systems and photonic quantum technologies.

physics.optics↗

arXiv2024

Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge

Many real-world image recognition problems, such as diagnostic medical imaging exams, are "long-tailed" $\unicode{x2013}$ there are a few common findings followed by many more relatively rare conditions. In chest radiography, diagnosis is both a long-tailed and multi-label problem, as patients often present with multiple findings simultaneously. While researchers have begun to study the problem of long-tailed learning in medical image recognition, few have studied the interaction of label imbalance and label co-occurrence posed by long-tailed, multi-label disease classification. To engage with the research community on this emerging topic, we conducted an open challenge, CXR-LT, on long-tailed, multi-label thorax disease classification from chest X-rays (CXRs). We publicly release a large-scale benchmark dataset of over 350,000 CXRs, each labeled with at least one of 26 clinical findings following a long-tailed distribution. We synthesize common themes of top-performing solutions, providing practical recommendations for long-tailed, multi-label medical image classification. Finally, we use these insights to propose a path forward involving vision-language foundation models for few- and zero-shot disease classification.

cs.CV↗

arXiv2023

Seamless monolithic three-dimensional integration of single-crystalline films by growth

The demand for the three-dimensional (3D) integration of electronic components is on a steady rise. The through-silicon-via (TSV) technique emerges as the only viable method for integrating single-crystalline device components in a 3D format, despite encountering significant processing challenges. While monolithic 3D (M3D) integration schemes show promise, the seamless connection of single-crystalline semiconductors without intervening wafers has yet to be demonstrated. This challenge arises from the inherent difficulty of growing single crystals on amorphous or polycrystalline surfaces post the back-end-of-the-line process at low temperatures to preserve the underlying circuitry. Consequently, a practical growth-based solution for M3D of single crystals remains elusive. Here, we present a method for growing single-crystalline channel materials, specifically composed of transition metal dichalcogenides, on amorphous and polycrystalline surfaces at temperatures lower than 400 °C. Building on this developed technique, we demonstrate the seamless monolithic integration of vertical single-crystalline logic transistor arrays. This accomplishment leads to the development of unprecedented vertical CMOS arrays, thereby constructing vertical inverters. Ultimately, this achievement sets the stage to pave the way for M3D integration of various electronic and optoelectronic hardware in the form of single crystals.

physics.app-ph↗