Centaur: A Chiplet-based, Hybrid Sparse-Dense Accelerator for Personalized Recommendations. - Ranggi Hwang; Taehun Kim; Youngeun Kwon; Minsoo Rhu (ISCA, 2020)

DiVa: An Accelerator for Differentially Private Machine Learning. - Beomsik Park; Ranggi Hwang; Dongho Yoon; Yoonhyuk Choi; Minsoo Rhu (MICRO, 2022)

GROW: A Row-Stationary Sparse-Dense GEMM Accelerator for Memory-Efficient Graph Convolutional Neural Networks. - Ranggi Hwang; Minhoo Kang; Jiwon Lee; Dongyun Kam; Youngjoo Lee; Minsoo Rhu (HPCA, 2023)

HAMMER: Hardware-Friendly Approximate Computing for Self-Attention With Mean-Redistribution And Linearization. - Seonho Lee; Ranggi Hwang; Jongse Park; Minsoo Rhu (IEEE Comput. Archit. Lett., 2023)

LazyDP: Co-Designing Algorithm-Software for Scalable Training of Differentially Private Recommendation Models. - Juntaek Lim; Youngeun Kwon; Ranggi Hwang; Kiwan Maeng; G. Edward Suh; Minsoo Rhu (ASPLOS (2), 2024)

Pre-gated MoE: An Algorithm-System Co-Design for Fast and Scalable Mixture-of-Expert Inference. - Ranggi Hwang; Jianyu Wei; Shijie Cao; Changho Hwang; Xiaohu Tang; Ting Cao; Mao Yang 004 (ISCA, 2024)

Debunking the CUDA Myth Towards GPU-based AI Systems: Evaluation of the Performance and Programmability of Intel's Gaudi NPU for AI Model Serving. - Yunjae Lee; Juntaek Lim; Jehyeon Bang; Eunyeong Cho; Huijong Jeong; Taesu Kim; Hyungjun Kim; Joonhyung Lee; Jinseop Im; Ranggi Hwang; Se Jung Kwon; Dongsoo Lee; et al. (ISCA, 2025)

Debunking the CUDA Myth Towards GPU-based AI Systems. - Yunjae Lee; Juntaek Lim; Jehyeon Bang; Eunyeong Cho; Huijong Jeong; Taesu Kim; Hyungjun Kim; Joonhyung Lee; Jinseop Im; Ranggi Hwang; Se Jung Kwon; Dongsoo Lee; et al. (CoRR, 2025)