Hyunwoo Oh

Hello there! I’m Hyunwoo Oh.

I’m a PhD student in Computer Science at the University of California, Irvine, supervised by Prof. Mohsen Imani @ BIASLab.

My research sits at the intersection of computer architecture, machine learning, and hardware–software co-design. I build efficient systems that scale emerging AI models—such as large language models, multimodal models, vision transformers, and GNNs—under tight performance, energy, and cost constraints.

I specialize in architecture-level co-design of algorithms and hardware: CPU ISA extensions and SIMD kernels for low-precision inference, FPGA and ASIC accelerators for multimodal AI, and system-level designs for real-time embedded sensing. I enjoy working across the stack, from quantization and model optimization down to RTL, FPGA prototyping, and chip tape-outs.

Previously, I was a Junior Engineer at Hanwha Systems, a leading Korean defense electronics company, where I designed SoC FPGA–based image processing pipelines, developed RTOS software, and optimized compute kernels for heterogeneous SoCs for infrared imaging systems.

Before starting my PhD, I received my M.S. in Electronic Engineering from Seoul National University of Science and Technology, advised by Prof. Seung Eun Lee. My master’s work explored processor architectures for posit arithmetic and domain-specific parallel accelerators, mostly realized on FPGAs and later fabricated as ASICs.

All Publications Curriculum Vitae

selected publications

2026

DATE
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, Hanning Chen, Tamoghno Das, Sanggeon Yun, Suyeon Jang, Andrew Ding, Nikil Dutt, and Mohsen Imani

Design, Automation and Test in Europe Conference (DATE), Verona, Italy, Apr 2026, pp. 1–7

Abstract BibTeX HTML

Recent advances in LLMs have outpaced the computational and memory capacities of edge platforms that primarily employ CPUs, thereby challenging efficient and scalable deployment. While ternary quantization enables significant resource savings, existing CPU solutions rely heavily on memory-based lookup tables (LUTs) which limit scalability, and FPGA or GPU accelerators remain impractical for edge use. This paper presents T-SAR, the first framework to achieve scalable ternary LLM inference on CPUs by repurposing the SIMD register file for dynamic, in-register LUT generation with minimal hardware modifications. T-SAR eliminates memory bottlenecks and maximizes data-level parallelism, delivering 5.6-24.5x and 1.1-86.2x improvements in GEMM latency and GEMV throughput, respectively, with only 3.2% power and 1.4% area overheads in SIMD units. T-SAR achieves up to 2.5-4.9x the energy efficiency of an NVIDIA Jetson AGX Orin, establishing a practical approach for efficient LLM inference on edge platforms.
@inproceedings{oh_tsar_2026, address = {Verona, Italy}, title = {{T-SAR: A Full-Stack Co-design for CPU-Only Ternary LLM Inference via In-Place SIMD ALU Reorganization}}, isbn = {}, url = {}, doi = {}, booktitle = {{Design, Automation and Test in Europe Conference (DATE)}}, author = {Oh, Hyunwoo and Nam, KyungIn and Bhattacharjya, Rajat and Chen, Hanning and Das, Tamoghno and Yun, Sanggeon and Jang, Suyeon and Ding, Andrew and Dutt, Nikil and Imani, Mohsen}, month = apr, year = {2026}, pages = {1--7}, }
DATE
QUILL: An Algorithm-Architecture Co-Design for Cache-Local Deformable Attention

Hyunwoo Oh, Hanning Chen, Sanggeon Yun, Yang Ni, Wenjun Huang, Tamoghno Das, Suyeon Jang, and Mohsen Imani

Design, Automation and Test in Europe Conference (DATE), Verona, Italy, Apr 2026, pp. 1–7

Abstract BibTeX HTML

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-aware accelerator that turns deformable attention into cache-friendly, single-pass work. At its core, Distance-based Out-of-Order Querying (DOOQ) orders queries by spatial proximity; the look-ahead drives a region prefetch into an alternate buffer–forming a schedule-aware prefetch loop that overlaps memory and compute. A fused MSDeformAttn engine executes interpolation, Softmax, aggregation, and the final projection (W”m) in one pass without spilling intermediates, while small tensors are kept on-chip and surrounding dense layers run on integrated GEMMs. Implemented as RTL and evaluated end-to-end, QUILL achieves up to 7.29x higher throughput and 47.3x better energy efficiency than an RTX 4090, and exceeds prior accelerators by 3.26-9.82x in throughput and 2.01-6.07x in energy efficiency. With mixed-precision quantization, accuracy tracks FP32 within <=0.9 AP across Deformable and Sparse DETR variants. By converting sparsity into locality–and locality into utilization–QUILL delivers consistent, end-to-end speedups.
@inproceedings{oh_quill_2026, address = {Verona, Italy}, title = {{QUILL: An Algorithm-Architecture Co-Design for Cache-Local Deformable Attention}}, isbn = {}, url = {}, doi = {}, booktitle = {{Design, Automation and Test in Europe Conference (DATE)}}, author = {Oh, Hyunwoo and Chen, Hanning and Yun, Sanggeon and Ni, Yang and Huang, Wenjun and Das, Tamoghno and Jang, Suyeon and Imani, Mohsen}, month = apr, year = {2026}, pages = {1--7}, }

2025

MM
LVLM_CSP: Accelerating Large Vision Language Models via Clustering, Scattering, and Pruning for Reasoning Segmentation

Hanning Chen, Yang Ni, Wenjun Huang, Hyunwoo Oh, Yezi Liu, Tamoghno Das, and Mohsen Imani

ACM International Conference on Multimedia (MM), Dublin, Ireland, Apr 2025, pp. 3932–3941

Abstract BibTeX

Large Vision Language Models (LVLMs) have been widely adopted to guide vision foundation models in performing reasoning segmentation tasks, achieving impressive performance. However, the substantial computational overhead associated with LVLMs presents a new challenge. The primary source of this computational cost arises from processing hundreds of image tokens. Therefore, an effective strategy to mitigate such overhead is to reduce the number of image tokens-a process known as image token pruning. Previous studies on image token pruning for LVLMs have primarily focused on high-level visual understanding tasks, such as visual question answering and image captioning. In contrast, guiding vision foundation models to generate accurate visual masks based on textual queries demands precise semantic and spatial reasoning capabilities. Consequently, pruning methods must carefully control individual image tokens throughout the LVLM reasoning process. Our empirical analysis reveals that existing methods struggle to adequately balance reductions in computational overhead with the necessity to maintain high segmentation accuracy. In this work, we propose LVLM_CSP, a novel training-free visual token pruning method specifically designed for LVLM-based reasoning segmentation tasks. LVLM_CSP consists of three stages: clustering, scattering, and pruning. Initially, the LVLM performs coarse-grained visual reasoning using a subset of selected image tokens. Next, fine-grained reasoning is conducted, and finally, most visual tokens are pruned in the last stage. Extensive experiments demonstrate that LVLM_CSP achieves a 65% reduction in image token inference FLOPs with virtually no accuracy degradation, and a 70% reduction with only a minor 1% drop in accuracy on the 7B LVLM.
@inproceedings{10.1145/3746027.3755243, address = {Dublin, Ireland}, title = {{LVLM_CSP: Accelerating Large Vision Language Models via Clustering, Scattering, and Pruning for Reasoning Segmentation}}, author = {Chen, Hanning and Ni, Yang and Huang, Wenjun and Oh, Hyunwoo and Liu, Yezi and Das, Tamoghno and Imani, Mohsen}, isbn = {9798400720352}, url = {https://doi.org/10.1145/3746027.3755243}, doi = {10.1145/3746027.3755243}, booktitle = {ACM International Conference on Multimedia (MM)}, year = {2025}, pages = {3932–3941}, }
DAC
iTaskSense: Task-Oriented Object Detection in Resource-Constrained Environments

SungHeon Jeong, Hamza Errahmouni Barkam, Hyunwoo Oh, Hanning Chen, Tamoghno Das, Zhen Ye, and Mohsen Imani

ACM/IEEE Design Automation Conference (DAC), San Francisco, CA, USA, Jun 2025, pp. 1–7

Abstract BibTeX HTML

Task-oriented object detection is increasingly essential for intelligent sensing applications, enabling AI systems to operate autonomously in complex, real-world environments such as autonomous driving, healthcare, and industrial automation. Conventional models often struggle with generalization, requiring vast datasets to accurately detect objects within diverse contexts. In this work, we introduce iTask, a taskoriented object detection framework that leverages large language models (LLMs) to generalize efficiently from limited samples by generating an abstract knowledge graph. This graph encapsulates essential task attributes, allowing iTask to identify objects based on high-level characteristics rather than extensive data, making it possible to adapt to complex mission requirements with minimal samples. iTask addresses the challenges of high computational cost and resource limitations in vision-language models by offering two configuration models: a distilled, task-specific vision transformer optimized for high accuracy in defined tasks, and a quantized version of the model for broader applicability across multiple tasks. Additionally, we designed a hardware acceleration circuit to support real-time processing, essential for edge devices that require low latency and efficient task execution. Our evaluations show that the task-specific configuration achieves a 15% higher accuracy over the quantized configuration in specific scenarios, while the quantized model provides robust multi-task performance. The hardware-accelerated iTask system achieves a 3.5x speedup and a 40% reduction in energy consumption compared to GPU-based implementations. These results demonstrate that iTask ’s dual-configuration approach and situational adaptability offer a scalable solution for task-specific object detection, providing robust and efficient performance in resource-constrained environments.
@inproceedings{jeong_itask_2025, address = {San Francisco, CA, USA}, title = {{iTaskSense: Task-Oriented Object Detection in Resource-Constrained Environments}}, isbn = {}, doi = {10.1109/DAC63849.2025.11133060}, booktitle = {{ACM/IEEE Design Automation Conference (DAC)}}, author = {Jeong, SungHeon and Barkam, Hamza Errahmouni and Oh, Hyunwoo and Chen, Hanning and Das, Tamoghno and Ye, Zhen and Imani, Mohsen}, month = jun, year = {2025}, pages = {1--7}, }
FCCM
A Multimodal AI Acceleration with Dynamic Pruning and Run-time Configuration

Hyun Woo Oh, Hanning Chen, Sanggeon Yun, Yang Ni, Behnam Khaleghi, Fei Wen, and Mohsen Imani

IEEE International Symposium on Field-Programmable Custom Computing Machines(FCCM), Fayetteville, AR, USA, May 2025

Abstract BibTeX HTML PDF Poster

The computational diversity of multimodal AI workloads—spanning vision transformers (ViTs), graph neural networks (GNNs), CNNs, and transformer-based NLP—poses a fundamental challenge to embedded acceleration platforms. We propose a fully integrated FPGA-based acceleration framework that addresses this heterogeneity via compile-time and runtime configurability. Our system introduces a reconfigurable processing unit (RPU) capable of executing dense and sparse matrix operations (DDMM, SpMM, SDDMM), a scalable top-k pruning engine for ViTs, and a domain-specific compiler for hardware-software co-design. The architecture supports real-time configuration without reloading bitstreams, enabling unified deployment across tasks. Implementations on Xilinx U50 and ZCU104 demonstrate up to 22.57× and 6.86× latency reductions versus RTX 4090 and Jetson Orin Nano, respectively, validating the design’s efficiency for real-time, resource-limited environments.
@inproceedings{oh_multimodal_2025, address = {Fayetteville, AR, USA}, title = {{A Multimodal AI Acceleration with Dynamic Pruning and Run-time Configuration}}, isbn = {}, doi = {10.1109/FCCM62733.2025.00072}, booktitle = {{IEEE International Symposium on Field-Programmable Custom Computing Machines(FCCM)}}, author = {Oh, Hyun Woo and Chen, Hanning and Yun, Sanggeon and Ni, Yang and Khaleghi, Behnam and Wen, Fei and Imani, Mohsen}, month = may, year = {2025}, }

2024

RTCSA
A Compact Real-Time Thermal Imaging System Based on Heterogeneous System-on-Chip

Hyun Woo Oh, Cheol-Ho Choi, Jeong Woo Cha, Hyunmin Choi, Jung-Ho Shin, and Joon Hwan Han

IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA), Sokcho, Korea, Aug 2024, pp. 97–107

Abstract BibTeX HTML

This paper presents a real-time embedded thermal imaging system architecture for compact, energy-efficient, high-quality imaging utilizing heterogeneous system-on-chip and uncooled infrared focal plane arrays (IRFPAs). In contrast to previous systems that organized separate devices for complex image processing, our system provides integrated image processing support for robust sensor-to-surveillance. We organized the image processing architecture into two algorithm stacks: a non-uniformity correction stack to mitigate the distinctive noise vulnerability of uncooled IRFPAs and an image enhancement stack, which includes contrast enhancement and frame-level temporal noise filters. We optimized the algorithms for domain-specific factors, including asymmetric multiprocessing (AMP), cache organization, single instruction multiple data (SIMD) instructions, and very long instruction word (VLIW) architectures. The implementation on TI TDA3x SoC demonstrates that our system can process 640×480, 60 frames per second (FPS) videos at a peak core load of 57.5% while consuming power less than 2.2 W for the entire system, denoting the possibility of processing the 1280×1024, 30 FPS videos from the state-of-the-art IRFPAs.
@inproceedings{oh_compact_2024, address = {Sokcho, Korea}, title = {A {Compact} {Real}-{Time} {Thermal} {Imaging} {System} {Based} on {Heterogeneous} {System}-on-{Chip}}, isbn = {979-8-3503-8795-7}, doi = {10.1109/RTCSA62462.2024.00023}, booktitle = {{IEEE} {International} {Conference} on {Embedded} and {Real}-{Time} {Computing} {Systems} and {Applications} ({RTCSA})}, publisher = {IEEE}, author = {Oh, Hyun Woo and Choi, Cheol-Ho and Cha, Jeong Woo and Choi, Hyunmin and Shin, Jung-Ho and Han, Joon Hwan}, month = aug, year = {2024}, pages = {97--107}, }
TCAS-II
DL-Sort: A Hybrid Approach to Scalable Hardware-Accelerated Fully-Streaming Sorting

Hyun Woo Oh, Joungmin Park, and Seung Eun Lee

IEEE Transactions on Circuits and Systems II: Express Briefs, May 2024, pp. 2549–2553

Abstract BibTeX HTML PDF Code

Designing high-performance hardware sorter for resource-constrained systems is challenging due to physical limitations and the need to balance streaming bandwidth with memory throughput. This paper introduces a novel, scalable hardware sorter architecture with fully-streaming support and an accompanying RTL generator to provide versatile, energy-efficient hardware acceleration. Our solution employs a dual-layer architecture consisting of a parallel one-way linear insertion sorter (OLIS) for bandwidth optimization and a cyclic bitonic merge network (CBMN) for a compact, high-throughput implementation. Furthermore, we developed the RTL generator written in Chisel to provide the agile implementation of the scalable architecture. Experimental results targeting the Xilinx XVU37P-FSVH2892-2L-E FPGA show that our design achieves up to 126.26% increase in throughput and 68.46% decrease in latency, with an area increment of no more than 132.94% for LUTs, and a decrement of up to 79.84% for flip-flops, compared to state-of-the-art streaming sorter.
@article{oh_dl-sort_2024, title = {{DL-Sort}: {A} {Hybrid} {Approach} {to} {Scalable} {Hardware}-{Accelerated} {Fully}-{Streaming} {Sorting}}, volume = {71}, number = {5}, issn = {1549-7747}, url = {https://ieeexplore.ieee.org/document/10472626}, doi = {10.1109/TCSII.2024.3377255}, journal = {IEEE Transactions on Circuits and Systems II: Express Briefs}, author = {Oh, Hyun Woo and Park, Joungmin and and Lee, Seung Eun}, month = may, year = {2024}, pages = {2549--2553}, }

2023

ISLPED
RF2P: A Lightweight RISC Processor Optimized for Rapid Migration from IEEE-754 to Posit

Hyun Woo Oh, Seongmo An, Won Sik Jeong, and Seung Eun Lee

ACM/IEEE International Symposium on Low Power Electronics and Design (ISLPED), Vienna, Austria, Aug 2023, pp. 1–6

Abstract BibTeX HTML PDF Slides

This paper presents a lightweight processor and evaluation platform for migrating from IEEE-754 to posit arithmetic, with an optimized posit arithmetic unit (PAU) supporting existing floating-point instructions. The PAU features a reconfigurable divider architecture for diverse operating conditions and lightweight square root logic. The platform includes a posit-optimized compiler, divider generator, JTAG environment builder, and programmable logic controller. The experimental results demonstrate the successful execution of legacy IEEE-754 code with a small additional workload and up to 60.09 times the performance improvement through hardware acceleration. Additionally, the PAU and divider consume 11.00% and 57.87% fewer LUTs, respectively, compared to the best prior works.
@inproceedings{oh_rf2p_2023, address = {Vienna, Austria}, title = {{RF2P}: {A} {Lightweight} {RISC} {Processor} {Optimized} for {Rapid} {Migration} from {IEEE}-754 to {Posit}}, isbn = {979-8-3503-1175-4}, url = {https://ieeexplore.ieee.org/document/10244582/}, doi = {10.1109/ISLPED58423.2023.10244582}, booktitle = {{ACM}/{IEEE} {International} {Symposium} on {Low} {Power} {Electronics} and {Design} ({ISLPED})}, publisher = {IEEE}, author = {Oh, Hyun Woo and An, Seongmo and Jeong, Won Sik and Lee, Seung Eun}, month = aug, year = {2023}, pages = {1--6}, }