TurboFlux: A Fast Continuous Subgraph Matching System for Streaming Graph Data. - Kyoungmin Kim 002; In Seo; Wook-Shin Han; Jeong-Hoon Lee 004; Sungpack Hong; Hassan Chafi; Hyungyu Shin; Geonhwa Jeong (SIGMOD Conference, 2018)

ConfuciuX: Autonomous Hardware Resource Assignment for DNN Accelerators using Reinforcement Learning. - Sheng-Chun Kao; Geonhwa Jeong; Tushar Krishna (MICRO, 2020)

Union: A Unified HW-SW Co-Design Ecosystem in MLIR for Evaluating Tensor Operations on Spatial Accelerators. - Geonhwa Jeong; Gokcen Kestor; Prasanth Chatarasi; Angshuman Parashar; Po-An Tsai; Sivasankaran Rajamanickam; Roberto Gioiosa; Tushar Krishna (PACT, 2021)

Bridging the Frequency Gap in Heterogeneous 3D SoCs through Technology-Specific NoC Router Architectures. - Jan Moritz Joseph; Lennart Bamberg; Geonhwa Jeong; Ruei-Ting Chien; Rainer Leupers; Alberto García-Ortiz; Tushar Krishna; Thilo Pionteck (ASP-DAC, 2021)

RASA: Efficient Register-Aware Systolic Array Matrix Engine for CPU. - Geonhwa Jeong; Eric Qin 001; Ananda Samajdar; Christopher J. Hughes; Sreenivas Subramoney; Hyesoon Kim; Tushar Krishna (DAC, 2021)

Extending Sparse Tensor Accelerators to Support Multiple Compression Formats. - Eric Qin 001; Geonhwa Jeong; William Won; Sheng-Chun Kao; Hyoukjun Kwon; Sudarshan Srinivasan; Dipankar Das 002; Gordon Euhyun Moon; Sivasankaran Rajamanickam; Tushar Krishna (IPDPS, 2021)

Understanding Data Compression in Warehouse-Scale Datacenter Services. - Geonhwa Jeong; Bikash Sharma; Nick Terrell; Abhishek Dhanotia; Zhiwei Zhao; Niket Agarwal; Arun Kejariwal; Tushar Krishna (ISPASS, 2022)

Evaluating Spatial Accelerator Architectures with Tiled Matrix-Matrix Multiplication. - Gordon Euhyun Moon; Hyoukjun Kwon; Geonhwa Jeong; Prasanth Chatarasi; Sivasankaran Rajamanickam; Tushar Krishna (IEEE Trans. Parallel Distributed Syst., 2022)

VEGETA: Vertically-Integrated Extensions for Sparse/Dense GEMM Tile Acceleration on CPUs. - Geonhwa Jeong; Sana Damani; Abhimanyu Rajeshkumar Bambhaniya; Eric Qin 001; Christopher J. Hughes; Sreenivas Subramoney; Hyesoon Kim; Tushar Krishna (HPCA, 2023)

Characterization of Data Compression in Datacenters. - Geonhwa Jeong; Bikash Sharma; Nick Terrell; Abhishek Dhanotia; Zhiwei Zhao; Niket Agarwal; Arun Kejariwal; Tushar Krishna (ISPASS, 2023)

Algorithm-Hardware Co-Design of Distribution-Aware Logarithmic-Posit Encodings for Efficient DNN Inference. - Akshat Ramachandran; Zishen Wan; Geonhwa Jeong; John Gustafson; Tushar Krishna (DAC, 2024)

GEAR: An Efficient Error Reduction Framework for KV Cache Compression in LLM Inference. - Hao Kang; Qingru Zhang; Souvik Kundu 009; Geonhwa Jeong; Zaoxing Liu; Tushar Krishna; Tuo Zhao (ENLSP, 2024)

Understanding Performance Implications of LLM Inference on CPUs. - Seonjin Na; Geonhwa Jeong; Byung Hoon Ahn; Jeffrey Young 001; Tushar Krishna; Hyesoon Kim (IISWC, 2024)

GEAR: An Efficient KV Cache Compression Recipe for Near-Lossless Generative Inference of LLM. - Hao Kang; Qingru Zhang; Souvik Kundu 009; Geonhwa Jeong; Zaoxing Liu; Tushar Krishna; Tuo Zhao (CoRR, 2024)

Abstracting Sparse DNN Acceleration via Structured Sparse Tensor Decomposition. - Geonhwa Jeong; Po-An Tsai; Abhimanyu Rajeshkumar Bambhaniya; Stephen W. Keckler; Tushar Krishna (CoRR, 2024)

Demystifying Platform Requirements for Diverse LLM Inference Use Cases. - Abhimanyu Bambhaniya; Ritik Raj; Geonhwa Jeong; Souvik Kundu 009; Sudarshan Srinivasan; Midhilesh Elavazhagan; Madhu Kumar; Tushar Krishna (CoRR, 2024)

SDQ: Sparse Decomposed Quantization for LLM Inference. - Geonhwa Jeong; Po-An Tsai; Stephen W. Keckler; Tushar Krishna (CoRR, 2024)

Accelerating Transformer Inference and Training with 2:4 Activation Sparsity. - Daniel Haziza; Timothy Chou; Dhruv Choudhary; Luca Wehrstedt; Francisco Massa; Jiecao Yu; Geonhwa Jeong; Supriya Rao; Patrick Labatut; Jesse Cai (CoRR, 2025)