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Ipoom Jeong

Yonsei University · Computer Science

About the Lab

Professor Ipoom Jeong's research lab focuses on computer architecture and hardware-software co-design, with a strong emphasis on accelerating datacenter workloads through specialized hardware accelerators and advanced interconnect technologies. The lab explores innovations in CXL-based memory systems, on-chip accelerators like Intel's Data Streaming Accelerator, and novel processor microarchitectures such as SpecInO to improve performance and efficiency. Their work also investigates hardware security vulnerabilities, particularly Row Hammer-induced memory errors, and their implications for system reliability. The lab bridges cutting-edge hardware advancements with real-world deployment challenges in hyperscale computing environments.

CXLhardware acceleratorsmemory systemsprocessor microarchitecturehardware security

Research Overview

Papers
28
Total Citations
224
Papers (5y)
24
Primary Field
Computer Science

Research Output Trend

Figures are computed from collected data and may differ slightly.

Publications per year (5y)
24total
2022
2023
2024
2025
2026
Citations per year (5y)
210total
20222023202420252026

Selected Papers

15
1
Preprint|106 citations·2023
Demystifying CXL Memory with Genuine CXL-Ready Systems and Devices
Yan Sun, Yifan Yuan, Zeduo Yu, Reese Kuper, Chihun Song, Jinghan Huang, Houxiang Ji, Siddharth Agarwal, Jiaqi Lou, Ipoom Jeong, Ren Wang, Jung Ho Ahn
OA

The ever-growing demands for memory with larger capacity and higher bandwidth have driven recent innovations on memory expansion and disaggregation technologies based on Compute eXpress Link (CXL). Especially, CXL-based memory expansion technology has recently gained notable attention for its ability not only to economically expand memory capacity and bandwidth but also to decouple memory technologies from a specific memory interface of the CPU. However, since CXL memory devices have not been wi

Hardware and ArchitectureComputer Science
2
Article|17 citations·2024
A Quantitative Analysis and Guidelines of Data Streaming Accelerator in Modern Intel Xeon Scalable Processors
Reese Kuper, Ipoom Jeong, Yifan Yuan, Ren Wang, Narayan Ranganathan, Nikhil Rao, Jiayu Hu, Sanjay Kumar, Philip Lantz, Nam Sung Kim
OA

As semiconductor power density is no longer constant with the technology process scaling down, we need different solutions if we are to continue scaling application performance. To this end, modern CPUs are integrating capable data accelerators on the chip, aiming to improve performance and efficiency for a wide range of applications and usages. One such accelerator is the Intel® Data Streaming Accelerator (DSA) introduced since Intel® 4th Generation Xeon® Scalable CPUs (Sapphire Rapids). DSA ta

Information SystemsComputer Science
3
Article|16 citations·2024
Intel Accelerators Ecosystem: An SoC-Oriented Perspective : Industry Product
Yifan Yuan, Ren Wang, Narayan Ranganathan, Nikhil Rao, Sanjay Kumar, Philip Lantz, Vivekananthan Sanjeepan, Jorge Cabrera‐Gámez, Atul Kwatra, Rajesh Sankaran, Ipoom Jeong, Nam Sung Kim

A growing demand for hyperscale services has compelled hyperscalers to deploy more compute resources at an unprecedented pace, further accelerated by the demise of Dennard scaling. Meanwhile, a considerable portion of the compute resources are consumed to execute common functions present across the hyperscale services, i.e., datacenter taxes. These challenges motivated many to explore specialized accelerators for these functions. Leading such a technology trend, Intel has integrated diverse on-c

Hardware and ArchitectureComputer Science
4
Article|13 citations·2020
CASINO Core Microarchitecture: Generating Out-of-Order Schedules Using Cascaded In-Order Scheduling Windows
Ipoom Jeong, Seihoon Park, Changmin Lee, Won Woo Ro

The performance gap between in-order (InO) and out-of-order (OoO) cores comes from the ability to dynamically create highly optimized instruction issue schedules. In this work, we observe that a significant amount of performance benefit of OoO scheduling can also be attained by supplementing a traditional InO core with a small and speculative instruction scheduling window, namely SpecInO. SpecInO monitors a small set of instructions ahead of a conventional InO scheduling window, aiming at issuin

Hardware and ArchitectureComputer Science
5
Article|11 citations·2024
Demystifying a CXL Type-2 Device: A Heterogeneous Cooperative Computing Perspective
Houxiang Ji, Srikar Vanavasam, Yang Zhou, Xia Qin, Jinghan Huang, Yifan Yuan, Ren Wang, Pramod Gupta, Bhushan Chitlur, Ipoom Jeong, Nam Sung Kim

CXL is the latest interconnect technology built on PCIe, providing three protocols to facilitate three distinct types of devices, each with unique capabilities. Among these devices, a CXL Type-2 device has become commercially available, followed by CXL Type-3 devices. Therefore, it is timely to understand capabilities and characteristics of the CXL Type-2 device, as well as explore suitable applications. In this work, first, we delve into three key features of a CXL Type-2 device: cache-coherent

Information Systems and ManagementDecision Sciences
6
Article|9 citations·2024
TAROT: A CXL SmartNIC-Based Defense Against Multi-bit Errors by Row-Hammer Attacks
Chihun Song, Michael Jaemin Kim, Tianchen Wang, Houxiang Ji, Jinghan Huang, Ipoom Jeong, Jaehyun Park, Hwayong Nam, Minbok Wi, Jung Ho Ahn, Nam Sung Kim

Row Hammer (RH) has been demonstrated as a security vulnerability in modern systems. Although commodity CPUs can handle RH-induced single-bit errors in DRAM through ECC, RH can still give rise to multi-bit uncorrectable errors (UEs) and crash the systems. Meanwhile, recent work has indicated that the DRAM cells vulnerable to RH are determined by manufacturing imperfections and resulting defects. Taking one step further from the recent work, we first conduct RH experiments on contemporary DRAM mo

Hardware and ArchitectureComputer Science
7
Article|7 citations·2024
ScaleCache: A Scalable Page Cache for Multiple Solid-State Drives
Kiet Tuan Pham, Seokjoo Cho, Sangjin Lee, Lan Anh Nguyen, Hyeongi Yeo, Ipoom Jeong, Sungjin Lee, Nam Sung Kim, Yongseok Son

This paper presents a scalable page cache called ScaleCache for improving SSD scalability. Specifically, we first propose a concurrent data structure of page cache based on XArray (ccXArray) to enable access and update the page cache concurrently. Second, we introduce a direct page flush (dflush) which directly flushes pages to storage devices in a parallel and opportunistic manner. We implement ScaleCache with two techniques in the Linux kernel and evaluate it on a 64-core machine with eight NV

Computer Networks and CommunicationsComputer Science
8
Article|6 citations·2022
Reconstructing Out-of-Order Issue Queue
Ipoom Jeong, Jiwon Lee, Myung Kuk Yoon, Won Woo Ro

Out-of-order cores provide high performance at the cost of energy efficiency. Dynamic scheduling is one of the major contributors to this: generating highly optimized issue schedules considering both data dependences and underlying execution resources, but relying heavily on complex wakeup and select operations of an out-of-order issue queue (IQ). For decades, researchers have proposed several complexity-effective dynamic scheduling schemes by leveraging the energy efficiency of an in-order IQ.

Hardware and ArchitectureComputer Science
9
Article|6 citations·2024
HAL: Hardware-assisted Load Balancing for Energy-efficient SNIC-Host Cooperative Computing
Jinghan Huang, Jiaqi Lou, Srikar Vanavasam, Xinhao Kong, Houxiang Ji, Ipoom Jeong, Danyang Zhuo, Eun Kyung Lee, Nam Sung Kim

A typical SmartNIC (SNIC) integrates a processor comprising Arm CPU and accelerators with a conventional NIC. The processor is designed to energy-efficiently execute network functions frequently used by datacenter applications. With such a processor, the SNIC has promised to notably improve the system-wide energy efficiency of datacenter servers. Nevertheless, the latest trend of integrating accelerators into server CPUs for these functions sparks a question on the SNIC processor’s superiority o

Hardware and ArchitectureComputer Science
10
Article|6 citations·2022
CASH-RF: A Compiler-Assisted Hierarchical Register File in GPUs
Yunho Oh, Ipoom Jeong, Won Woo Ro, Myung Kuk Yoon
SJR Q2IEEE Embedded Systems Letters

Spin-transfer torque magnetic random-access memory (STT-MRAM) is an emerging nonvolatile memory technology that has been received significant attention due to its higher density and lower leakage current over SRAM. One compelling use case is to employ STT-MRAM as a graphics processing unit (GPU) register file (RF) to reduce its massive energy consumption. One critical challenge is that STT-MRAM has longer access latency and higher dynamic power consumption than SRAM, which motivates the hierarch

Atomic and Molecular Physics, and OpticsPhysics and Astronomy
11
Article|4 citations·2025
UPP: Universal Predicate Pushdown to Smart Storage
Ipoom Jeong, Jinghan Huang, Chuxuan Hu, Dohyun Park, Jaeyoung Kang, Nam Sung Kim, Yongjoo Park
OA

In large-scale analytics, in-storage processing (ISP) can significantly boost query performance by letting ISP engines (e.g., FPGAs) preselect only the relevant data before sending them to databases.This reduces the amount of not only data transfer between storage and host, but also database computation, facilitating faster query processing.However, existing ISP solutions cannot effectively support a wide range of modern analytical queries because they only support simple combinations of frequen

Computer Networks and CommunicationsComputer Science
12
Article|4 citations·2022
TEA-RC: Thread Context-Aware Register Cache for GPUs
Ipoom Jeong, Yunho Oh, Won Woo Ro, Myung Kuk Yoon
SJR Q1IEEE AccessOA

Graphics processing units (GPUs) achieve high throughput by exploiting a high degree of thread-level parallelism (TLP). To support such high TLP, GPUs have a large-sized register file to store the context of all threads, consuming around 20% of total GPU energy. Several previous studies have attempted to minimize the energy consumption of the register file by implementing an emerging non-volatile memory (NVM), leveraging its higher density and lower leakage power over SRAMs. To amortize the cost

Hardware and ArchitectureComputer Science
13
Article|4 citations·2023
A convertible neural processor supporting adaptive quantization for real-time neural networks
Hongju Kal, Hyoseong Choi, Ipoom Jeong, Joon-Sung Yang, Won Woo Ro
SJR Q1Journal of Systems Architecture
Computer Vision and Pattern RecognitionComputer Science
14
Article|4 citations·2023
LADIO: Leakage-Aware Direct I/O for I/O-Intensive Workloads
Ipoom Jeong, Jiaqi Lou, Yongseok Son, Yongjoo Park, Yifan Yuan, Nam Sung Kim
SJR Q2IEEE Computer Architecture Letters

The advancement in I/O technology has posed an unprecedented demand for high-performance processing on I/O data, leading to the development of Data Direct I/O (DDIO) technology. DDIO improves I/O processing efficiency by directly injecting all inbound I/O data into the last-level cache (LLC) in cooperation with any type of I/O device. Nonetheless, in certain scenarios with more than one I/O applications, DDIO may have sub-optimal performance caused by interference inside the LLC, resulting in th

Computer Networks and CommunicationsComputer Science
15
Article|3 citations·2023
INTERPRET: Inter-Warp Register Reuse for GPU Tensor Core
Jae Seok Kwak, Myung Kuk Yoon, Ipoom Jeong, Seung‐Hyun Jin, Won Woo Ro

Tensor cores in the recent NVIDIA GPUs are under the spotlight due to their superior computation throughput for general matrix-matrix multiplication (GEMM) that has been widely used for deep learning applications. For massive-scale GEMMs, the entire matrix is practically divided into sub-matrices and assigned to multiple thread blocks and warps, and then processed by the tensor cores. Meanwhile, the same sub-matrix is regularly reused as an input to different sub-GEMMs, which causes redundant lo

Hardware and ArchitectureComputer Science

Research Areas

Hardware and ArchitectureComputer Networks and CommunicationsInformation SystemsInformation Systems and ManagementAtomic and Molecular Physics, and OpticsComputer Vision and Pattern Recognition

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