Accelerating Fully Homomorphic Encryption Through Architecture-Centric Analysis and Optimization. - Wonkyung Jung; Eojin Lee; Sangpyo Kim; Jongmin Kim 007; Namhoon Kim; Keewoo Lee; Chohong Min; Jung Hee Cheon; Jung Ho Ahn (IEEE Access, 2021)

BTS: an accelerator for bootstrappable fully homomorphic encryption. - Sangpyo Kim; Jongmin Kim 007; Michael Jaemin Kim; Wonkyung Jung; John Kim 001; Minsoo Rhu; Jung Ho Ahn (ISCA, 2022)

ARK: Fully Homomorphic Encryption Accelerator with Runtime Data Generation and Inter-Operation Key Reuse. - Jongmin Kim 007; Gwangho Lee; Sangpyo Kim; Gina Sohn; Minsoo Rhu; John Kim 001; Jung Ho Ahn (MICRO, 2022)

SHARP: A Short-Word Hierarchical Accelerator for Robust and Practical Fully Homomorphic Encryption. - Jongmin Kim 007; Sangpyo Kim; Jaewan Choi; Jaiyoung Park; Donghwan Kim; Jung Ho Ahn (ISCA, 2023)

HyPHEN: A Hybrid Packing Method and Optimizations for Homomorphic Encryption-Based Neural Networks. - Donghwan Kim; Jaiyoung Park; Jongmin Kim 007; Sangpyo Kim; Jung Ho Ahn (CoRR, 2023)

Toward Practical Privacy-Preserving Convolutional Neural Networks Exploiting Fully Homomorphic Encryption. - Jaiyoung Park; Donghwan Kim; Jongmin Kim 007; Sangpyo Kim; Wonkyung Jung; Jung Hee Cheon; Jung Ho Ahn (CoRR, 2023)

NeuJeans: Private Neural Network Inference with Joint Optimization of Convolution and Bootstrapping. - Jae Hyung Ju; Jaiyoung Park; Jongmin Kim 007; Donghwan Kim; Jung Ho Ahn (CoRR, 2023)

High-precision RNS-CKKS on fixed but smaller word-size architectures: theory and application. - Rashmi Agrawal 001; Jung Ho Ahn; Flávio Bergamaschi; Ro Cammarota; Jung Hee Cheon; Fillipe D. M. de Souza; Huijing Gong; Minsik Kang; Duhyeong Kim; Jongmin Kim 007; Hubert de Lassus; Jai Hyun Park; et al. (IACR Cryptol. ePrint Arch., 2023)

NeuJeans: Private Neural Network Inference with Joint Optimization of Convolution and FHE Bootstrapping. - Jae Hyung Ju; Jaiyoung Park; Jongmin Kim 007; Minsik Kang; Donghwan Kim; Jung Hee Cheon; Jung Ho Ahn (CCS, 2024)

Duplex: A Device for Large Language Models with Mixture of Experts, Grouped Query Attention, and Continuous Batching. - Sungmin Yun 001; Kwanhee Kyung; Juhwan Cho; Jaewan Choi; Jongmin Kim 007; Byeongho Kim; Sukhan Lee 002; Kyomin Sohn; Jung Ho Ahn (MICRO, 2024)

CiFHER: A Chiplet-Based FHE Accelerator with a Resizable Structure. - Sangpyo Kim; Jongmin Kim 007; Jaeyoung Choi; Jung Ho Ahn (SEED, 2024)

HyPHEN: A Hybrid Packing Method and Its Optimizations for Homomorphic Encryption-Based Neural Networks. - Donghwan Kim; Jaiyoung Park; Jongmin Kim 007; Sangpyo Kim; Jung Ho Ahn (IEEE Access, 2024)

Cheddar: A Swift Fully Homomorphic Encryption Library for CUDA GPUs. - Jongmin Kim 007; Wonseok Choi 015; Jung Ho Ahn (CoRR, 2024)

Anaheim: Architecture and Algorithms for Processing Fully Homomorphic Encryption in Memory. - Jongmin Kim 007; Sungmin Yun 001; Hyesung Ji; Wonseok Choi 015; Sangpyo Kim; Jung Ho Ahn (HPCA, 2025)

SHIP: A Shallow and Highly Parallelizable CKKS Bootstrapping Algorithm. - Jung Hee Cheon; Guillaume Hanrot; Jongmin Kim 007; Damien Stehlé (IACR Cryptol. ePrint Arch., 2025)

The New LLM Bottleneck: A Systems Perspective on Latent Attention and Mixture-of-Experts. - Sungmin Yun 001; Seonyong Park; Hwayong Nam; Younjoo Lee; Gunjun Lee; Kwanhee Kyung; Sangpyo Kim; Nam Sung Kim; Jongmin Kim 007; Hyungyo Kim; Juhwan Cho; Seungmin Baek; et al. (CoRR, 2025)