arXiv subjects

Jongmin Kim

Publications and source records attributed to Jongmin Kim.

At least 19 recordsLinked to original sources

arXiv2026

Keep-or-Drop? Adaptive Tokenizer for Compact Video Representation

Latent diffusion models have emerged as a dominant framework for high-fidelity image and video synthesis, operating in compact latent spaces with variational autoencoders (VAEs) to enhance computational efficiency without compromising visual quality. However, conventional VAEs are suboptimal for video data as they employ fixed compression ratios that cannot adapt to the varying complexity of spatio-temporal content. We present KATok (Keep-or-Drop? Adaptive Tokenizer for Compact Video Representation), a transformer-based VAE that incorporates an adaptive token selector which is jointly learned with latent tokens. By evaluating each token's content-richness as keep-or-drop probability, the token selector effectively discards uninformative tokens, naturally allowing data-dependent compression. Applying adaptive tokenization to diffusion models may cause spatial misalignment, as token dropping can disturb the original spatio-temporal structure. To alleviate this issue, we propose two position-prediction strategies: cascaded and joint generation, to ensure spatial consistency. We empirically show that our model achieves strong reconstruction and generation quality at a state-of-the-art compression ratio. Further analysis on video data reveals that this improvement is primarily achieved by reducing spatio-temporal redundancy and removing uninformative tokens, as supported by both quantitative and qualitative results.

cs.CV↗

arXiv2026

VIPIR: A Versatile GPU Framework for Integrating Private Information Retrieval Protocols

While private information retrieval (PIR) enables private database services by fully concealing access patterns, it simultaneously requires high computational throughput, large memory capacity, and substantial memory bandwidth. We introduce VIPIR, a versatile GPU framework that co-designs PIR protocols with GPU acceleration. We develop a unified analytic model showing that state-of-the-art PIR protocols fall into two categories with complementary limitations, and propose two protocols that flexibly combine techniques across these categories, overcoming the limitations of both classes. These protocols incorporate a GPU-friendly data compression method called expansion-based ring packing (ExpPack), which offers a high degree of parallelism and minimal communication cost. VIPIR applies further optimizations to core operations, including number-theoretic transforms (NTTs) and various matrix-matrix multiplications (GEMMs). Notably, we develop a tensor-core-based execution method for database multiplication by interpreting it as a mixed-integer-type GEMM. We also design memory-efficient scheduling methods that minimize intermediate buffers and enable multi-GPU scaling under memory capacity constraints. Overall, VIPIR achieves orders-of-magnitude higher throughput than prior PIR systems while reducing communication and memory overheads, making large-scale PIR practical.

cs.CR↗

arXiv2026

WHET: Welding Homomorphic Encryption to Accelerator Architectures

Fully homomorphic encryption (FHE) enables computations on encrypted data without decryption, offering strong data privacy at the expense of substantial computational and memory overheads. Prior efforts have steadily improved FHE performance through cryptographic and algorithmic enhancements or hardware acceleration, yet these two directions have progressed largely in isolation, hindering the full exploitation of available hardware capabilities. This work presents WHET, which introduces memory-centric, architecture-aware optimizations to better align cryptographic and algorithmic constructions with FHE accelerator architectures. We identify conventional FHE constructions as major sources of excessive working sets and heavy off-chip memory traffic. We propose accelerator-specific techniques, including fine-grained coefficient-to-slot transformation, plaintext compression, and intermediate modulus raising, to reduce the on-chip data footprint by minimizing temporary ciphertexts and plaintext loads. With these techniques applied, we observe additional opportunities to improve on-chip memory efficiency; hence, we introduce lightweight architectural refinements, including a special-purpose buffer and functional unit extensions. With these optimizations, WHET achieves 1.38-8.74$\times$ per-area performance improvements over state-of-the-art FHE accelerators and the first-ever sub-millisecond CKKS bootstrapping.

cs.CR↗

arXiv2026

Energy spectra and cascade in the spin turbulence of a driven spinor Bose-Einstein condensate

We investigate the spin-interaction energy spectrum of spin turbulence in a driven spinor Bose-Einstein condensate. Continuous spin driving of a spin-1 condensate produces a nonequilibrium steady state with spatially fluctuating magnetization. We observe a power-law scaling consistent with the $-7/3$ exponent predicted for spin-wave turbulence, which persists across our full range of drive strengths despite substantial changes in the spectral anisotropy. After switching off the drive, we track the free-decay evolution and find evidence consistent with a direct cascade of spin-interaction energy toward higher wavenumbers. These results establish an energy-spectral hallmark of spin turbulence and enable quantitative studies of cascade dynamics in spinor superfluids.

cond-mat.quant-gas↗

arXiv2026

Efficient ensemble randomization by tuning chaos in a nonlinear spin-1 system

We present an efficient scheme to randomize a spin-state ensemble in a nonlinear spin-1 system by tuning chaos with an external periodic drive. Without modulation, the system exhibits a mixed phase space featuring regular islands embedded in a chaotic sea, where global mixing is inhibited by energy conservation. Using numerical simulations, we demonstrate that weak modulation of a linear Zeeman field not only facilitates transport between different energy shells but also drives ensembles toward a Haar-random distribution over spin states. Under optimized conditions, complete randomization is achieved on a timescale set by the inverse nonlinear interaction energy. In the overdriven regime, randomization is unexpectedly suppressed at specific modulation amplitudes, accompanied by the formation of sticky regions in phase space. We attribute this behavior to the dynamical cancellation of the leading low-order harmonic component of the periodic drive. These results illustrate how time-periodic driving can be used to engineer chaotic systems and achieve controllable randomization in nonlinear spin systems.

quant-ph↗

arXiv2026

GPIR: Enabling Practical Private Information Retrieval with GPUs

Private information retrieval (PIR) allows private database queries; however, it is hindered by intense server-side computation and memory traffic. Numerous modern lattice-based PIR protocols consist of three phases: ExpandQuery (expanding a query into encrypted indices), RowSel (encrypted row selection), and ColTor (recursive "column tournament" for final selection). ExpandQuery and ColTor primarily perform number-theoretic transforms (NTTs), whereas RowSel reduces to large-scale independent matrix-matrix multiplications (GEMMs). GPUs are well suited for these tasks when combined with multi-client batching, which is necessary for high throughput. However, batching fundamentally reshapes the performance bottlenecks: while it amortizes database access costs, it expands working sets beyond the L2 cache capacity, causing divergent memory access behavior and excessive DRAM traffic. We present GPIR, a GPU-accelerated PIR system that rethinks kernel design, data layout, and execution scheduling. We introduce a stage-aware hybrid execution model that dynamically switches between operation-level kernels, which execute each primitive operation separately, and stage-level kernels, which fuse all operations within a stage into a single kernel to maximize on-chip data reuse. For RowSel, we resolve the mismatch between NTT-driven layouts and tiled GEMMs using a transposed-layout design with fine-grained pipelining. We further extend GPIR to multi-GPU systems, scaling throughput and database capacity with negligible communication overhead. GPIR achieves up to 297.2x higher throughput than PIRonGPU, the state-of-the-art GPU implementation.

cs.CR↗

arXiv2025

Theodosian: A Deep Dive into Memory-Hierarchy-Centric FHE Acceleration

Fully homomorphic encryption (FHE) enables secure computation on encrypted data, mitigating privacy concerns in cloud and edge environments. However, due to its high compute and memory demands, extensive acceleration research has been pursued across diverse hardware platforms, especially GPUs. In this paper, we perform a microarchitectural analysis of CKKS, a popular FHE scheme, on modern GPUs. Focusing on the memory hierarchy, we demonstrate that dominant kernels remain bound by the on-chip L2 cache despite its high bandwidth, exposing a persistent inner memory wall beyond the conventional off-chip DRAM bottleneck. Further, we reveal that the overall CKKS throughput is constrained by low per-kernel hardware utilization, caused by insufficient intra-kernel parallelism. Motivated by these findings, we introduce Theodosian, a set of complementary, memory-aware optimizations that improve cache efficiency and reduce runtime overheads. Theodosian achieves 1.45--1.83x performance improvements over a highly optimized baseline, Cheddar, across representative CKKS workloads. On an RTX 5090, we reduce the bootstrapping latency for 32,768 complex numbers from 22.1ms to 15.2ms, and further to 12.8ms with additional algorithmic optimizations, establishing a new state-of-the-art GPU performance to the best of our knowledge.

cs.CR↗

arXiv2025

IVE: An Accelerator for Single-Server Private Information Retrieval Using Versatile Processing Elements

Private information retrieval (PIR) is an essential cryptographic protocol for privacy-preserving applications, enabling a client to retrieve a record from a server's database without revealing which record was requested. Single-server PIR based on homomorphic encryption has particularly gained immense attention for its ease of deployment and reduced trust assumptions. However, single-server PIR remains impractical due to its high computational and memory bandwidth demands. Specifically, reading the entirety of large databases from storage, such as SSDs, severely limits its performance. To address this, we propose IVE, an accelerator for single-server PIR with a systematic extension that enables practical retrieval from large databases using DRAM. Recent advances in DRAM capacity allow PIR for large databases to be served entirely from DRAM, removing its dependence on storage bandwidth. Although the memory bandwidth bottleneck still remains, multi-client batching effectively amortizes database access costs across concurrent requests to improve throughput. However, client-specific data remains a bottleneck, whose bandwidth requirements ultimately limits performance. IVE overcomes this by employing a large on-chip scratchpad with an operation scheduling algorithm that maximizes data reuse, further boosting throughput. Additionally, we introduce sysNTTU, a versatile functional unit that enhances area efficiency without sacrificing performance. We also propose a heterogeneous memory system architecture, which enables a linear scaling of database sizes without a throughput degradation. Consequently, IVE achieves up to 1,275x higher throughput compared to prior PIR hardware solutions.

cs.CR↗

arXiv2025

Beyond Line-Level Filtering for the Pretraining Corpora of LLMs

While traditional line-level filtering techniques, such as line-level deduplication and trailing-punctuation filters, are commonly used, these basic methods can sometimes discard valuable content, negatively affecting downstream performance. In this paper, we introduce two methods-pattern-aware line-level deduplication (PLD) and pattern-aware trailing punctuation filtering (PTF)-by enhancing the conventional filtering techniques. Our approach not only considers line-level signals but also takes into account their sequential distribution across documents, enabling us to retain structurally important content that might otherwise be removed. We evaluate these proposed methods by training small language models (1 B parameters) in both English and Korean. The results demonstrate that our methods consistently improve performance on multiple-choice benchmarks and significantly enhance generative question-answering accuracy on both SQuAD v1 and KorQuAD v1.

cs.CL↗

arXiv2025

Rethinking LLM Inference Bottlenecks: Insights from Latent Attention and Mixture-of-Experts

Computational workloads composing traditional transformer models are starkly bifurcated. Multi-Head Attention (MHA) and Grouped-Query Attention are memory-bound due to low arithmetic intensity, while FeedForward Networks are compute-bound. This dichotomy has long motivated research into specialized hardware to mitigate the attention bottleneck. This paper argues that recent architectural advances in transformer models -- Multi-head Latent Attention (MLA) and Mixture of Experts (MoE) -- introduce new dominant bottlenecks, shifting the challenge away from memory-intensive attention. We make two key observations. First, the arithmetic intensity of MLA is over two orders of magnitude higher than that of MHA, moving it toward a compute-bound regime well-matched to modern accelerators such as GPUs. Second, distributing MoE experts across a pool of accelerators allows batching to tune their arithmetic intensity to that of dense layers, producing a more balanced computational profile. Consequently, the focus of hardware and system optimization should shift from attention acceleration to high-bandwidth interconnects and balancing expert workloads across accelerators.

cs.AR↗

arXiv2025

Thunder-LLM: Efficiently Adapting LLMs to Korean with Minimal Resources

Since state-of-the-art LLMs often underperform in languages other than English or Chinese, improving the capability of LLMs in new languages has become an essential task. Moreover, LLMs' entire end-to-end training process remains largely unknown to the public due to proprietary reasons, technical complexity, inconsistent documentation, and ethical considerations. The complete picture remains a closely guarded secret within the industry. This paper presents methods to adapt an existing English-based LLM to Korean in a low-budget scenario. We describe the entire end-to-end process: collecting Korean datasets, preprocessing the data, training the model, creating downstream benchmarks, and conducting evaluations. The evaluation results indicate that our method can effectively and cost-efficiently add new language capabilities to existing LLMs. Our new bilingual models, Thunder-LLM and Thunder-LLM-Ins, achieve superior Korean performance compared to state-of-the-art models while utilizing minimal data and computational resources. We share our comprehensive experience and make the code publicly available.

cs.CL↗

arXiv2025

Slot-MLLM: Object-Centric Visual Tokenization for Multimodal LLM

Recently, multimodal large language models (MLLMs) have emerged as a key approach in achieving artificial general intelligence. In particular, vision-language MLLMs have been developed to generate not only text but also visual outputs from multimodal inputs. This advancement requires efficient image tokens that LLMs can process effectively both in input and output. However, existing image tokenization methods for MLLMs typically capture only global abstract concepts or uniformly segmented image patches, restricting MLLMs' capability to effectively understand or generate detailed visual content, particularly at the object level. To address this limitation, we propose an object-centric visual tokenizer based on Slot Attention specifically for MLLMs. In particular, based on the Q-Former encoder, diffusion decoder, and residual vector quantization, our proposed discretized slot tokens can encode local visual details while maintaining high-level semantics, and also align with textual data to be integrated seamlessly within a unified next-token prediction framework of LLMs. The resulting Slot-MLLM demonstrates significant performance improvements over baselines with previous visual tokenizers across various vision-language tasks that entail local detailed comprehension and generation. Notably, this work is the first demonstration of the feasibility of object-centric slot attention performed with MLLMs and in-the-wild natural images.

cs.CV↗

arXiv2025

A Delayed Acceptance Auxiliary Variable MCMC for Spatial Models with Intractable Likelihood Function

A large class of spatial models contains intractable normalizing functions, such as spatial lattice models, interaction spatial point processes, and social network models. Bayesian inference for such models is challenging since the resulting posterior distribution is doubly intractable. Although auxiliary variable MCMC (AVM) algorithms are known to be the most practical, they are computationally expensive due to the repeated auxiliary variable simulations. To address this, we propose delayed-acceptance AVM (DA-AVM) methods, which can reduce the number of auxiliary variable simulations. The first stage of the kernel uses a cheap surrogate to decide whether to accept or reject the proposed parameter value. The second stage guarantees detailed balance with respect to the posterior. The auxiliary variable simulation is performed only on the parameters accepted in the first stage. We construct various surrogates specifically tailored for doubly intractable problems, including subsampling strategy, Gaussian process emulation, and frequentist estimator-based approximation. We validate our method through simulated and real data applications, demonstrating its practicality for complex spatial models.

stat.ME↗

arXiv2025

Enhancement of damping in a turbulent atomic Bose-Einstein condensate

Turbulence enhances momentum transport in classical fluids, effectively increasing their viscosity. We investigate an analogous effect in a superfluid by measuring the damping of collective oscillations in an atomic Bose-Einstein condensate (BEC) containing stationary spin-superflow turbulence. Using continuous spin driving to maintain turbulence in a spin-1 $^{23}$Na BEC, we excite its quadrupole mode and measure the damping rate over a range of temperatures. The damping consistently exceeds the Landau-damping rate expected for an equilibrium, non-turbulent BEC. The enhancement likely originates from two complementary processes: direct energy transfer from the mode to turbulent condensate fluctuations and turbulence-induced modification of the thermal cloud that amplifies Landau damping. These results establish collective-mode damping as a sensitive probe of momentum transport in superfluid turbulence.

cond-mat.quant-gas↗

arXiv2024

Duplex: A Device for Large Language Models with Mixture of Experts, Grouped Query Attention, and Continuous Batching

Large language models (LLMs) have emerged due to their capability to generate high-quality content across diverse contexts. To reduce their explosively increasing demands for computing resources, a mixture of experts (MoE) has emerged. The MoE layer enables exploiting a huge number of parameters with less computation. Applying state-of-the-art continuous batching increases throughput; however, it leads to frequent DRAM access in the MoE and attention layers. We observe that conventional computing devices have limitations when processing the MoE and attention layers, which dominate the total execution time and exhibit low arithmetic intensity (Op/B). Processing MoE layers only with devices targeting low-Op/B such as processing-in-memory (PIM) architectures is challenging due to the fluctuating Op/B in the MoE layer caused by continuous batching. To address these challenges, we propose Duplex, which comprises xPU tailored for high-Op/B and Logic-PIM to effectively perform low-Op/B operation within a single device. Duplex selects the most suitable processor based on the Op/B of each layer within LLMs. As the Op/B of the MoE layer is at least 1 and that of the attention layer has a value of 4-8 for grouped query attention, prior PIM architectures are not efficient, which place processing units inside DRAM dies and only target extremely low-Op/B (under one) operations. Based on recent trends, Logic-PIM adds more through-silicon vias (TSVs) to enable high-bandwidth communication between the DRAM die and the logic die and place powerful processing units on the logic die, which is best suited for handling low-Op/B operations ranging from few to a few dozens. To maximally utilize the xPU and Logic-PIM, we propose expert and attention co-processing.

cs.AR↗

arXiv2024

Cheddar: A Swift Fully Homomorphic Encryption Library Designed for GPU Architectures

Fully homomorphic encryption (FHE) frees cloud computing from privacy concerns by enabling secure computation on encrypted data. However, its substantial computational and memory overhead results in significantly slower performance compared to unencrypted processing. To mitigate this overhead, we present Cheddar, a high-performance FHE library for GPUs, achieving substantial speedups over previous GPU implementations. We systematically enable 32-bit FHE execution, leveraging the 32-bit integer datapath within GPUs. We optimize GPU kernels using efficient low-level primitives and algorithms tailored to specific GPU architectures. Further, we alleviate the memory bandwidth burden by adjusting common FHE operational sequences and extensively applying kernel fusion. Cheddar delivers performance improvements of 2.18--4.45$\times$ for representative FHE workloads compared to state-of-the-art GPU implementations.

cs.CR↗

arXiv2024

CiFHER: A Chiplet-Based FHE Accelerator with a Resizable Structure

Fully homomorphic encryption (FHE) is in the spotlight as a definitive solution for privacy, but the high computational overhead of FHE poses a challenge to its practical adoption. Although prior studies have attempted to design ASIC accelerators to mitigate the overhead, their designs require excessive chip resources (e.g., areas) to contain and process massive data for FHE operations. We propose CiFHER, a chiplet-based FHE accelerator with a resizable structure, to tackle the challenge with a cost-effective multi-chip module (MCM) design. First, we devise a flexible core architecture whose configuration is adjustable to conform to the global organization of chiplets and design constraints. Its distinctive feature is a composable functional unit providing varying computational throughput for the number-theoretic transform, the most dominant function in FHE. Then, we establish generalized data mapping methodologies to minimize the interconnect overhead when organizing the chips into the MCM package in a tiled manner, which becomes a significant bottleneck due to the packaging constraints. This study demonstrates that a CiFHER package composed of a number of compact chiplets provides performance comparable to state-of-the-art monolithic ASIC accelerators while significantly reducing the package-wide power consumption and manufacturing cost.

cs.AR↗

arXiv2024

Flattening a trapped atomic gas using a programmable optical potential in a feedback loop

We present a method for producing a flat, large-area Fermi gas of $^6$Li with a uniform area density. The method uses a programmable optical potential within a feedback loop to flatten the in-plane trapping potential for atoms. The optical potential is generated using a laser beam, whose intensity profile is adjusted by a spatial light modulator and optimized through measurements of the density distribution of the sample. The resulting planar sample exhibits a uniform area density within a region of about 480 $μ$m in diameter and the standard deviation of the trap bottom potential is estimated to be $\approx k_B \times$ 6.1 nK, which is less than 20$\%$ of the transverse confinement energy. We discuss a dimensional crossover toward 2D regime by reducing the number of atoms in the planar trap, including the effect of the spatial variation of the transverse trapping frequency in the large-area sample.

cond-mat.quant-gas↗