Jaiyoung Park

According to our database, Jaiyoung Park authored at least 15 papers between 2020 and 2026.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of three.

2026

DyLLM: Efficient Diffusion LLM Inference via Saliency-based Token Selection and Partial Attention.

Younjoo Lee, Junghoo Lee, Seungkyun Dan, Jaiyoung Park, Jung Ho Ahn

CoRR, March, 2026

Scaling up Privacy-Preserving ML: A CKKS Implementation of Llama-2-7B.

Jaiyoung Park, Sejin Park, Jai Hyun Park, Jung Ho Ahn, Jung Hee Cheon, Guillaume Hanrot, Jung Woo Kim, Minje Park, Damien Stehlé

CoRR, January, 2026

A Five-Year Journey to Accelerate Homomorphic Encryption With GPUs, Demonstrated by Sub-25-ms Convolutional Neural Network Inference.

Wonseok Choi, Sangpyo Kim, Jaiyoung Park, Jung Ho Ahn

IEEE Micro, 2026

Theodosian: A Deep Dive into Memory-Hierarchy-Centric FHE Acceleration.

Wonseok Choi, Hyunah Yu, Jongmin Kim, Hyesung Ji, Jaiyoung Park, Jung Ho Ahn

Proceedings of the IEEE International Symposium on Performance Analysis of Systems and Software, 2026

IVE: An Accelerator for Single-Server Private Information Retrieval Using Versatile Processing Elements.

Sangpyo Kim, Hyesung Ji, Jongmin Kim, Wonseok Choi, Jaiyoung Park, Jung Ho Ahn

Proceedings of the IEEE International Symposium on High Performance Computer Architecture, 2026

2025

Unlocking Private Computation at Scale: The Acceleration of Homomorphic Encryption.

Jaiyoung Park, Sangpyo Kim, Jongmin Kim, Jung Ho Ahn

Computer, December, 2025

From Tokens to Layers: Redefining Stall-Free Scheduling for LLM Serving with Layered Prefill.

Gunjun Lee, Jiwon Kim, Jaiyoung Park, Younjoo Lee, Jung Ho Ahn

CoRR, October, 2025

2024

HyPHEN: A Hybrid Packing Method and Its Optimizations for Homomorphic Encryption-Based Neural Networks.

Donghwan Kim, Jaiyoung Park, Jongmin Kim, Sangpyo Kim, Jung Ho Ahn

IEEE Access, 2024

NeuJeans: Private Neural Network Inference with Joint Optimization of Convolution and FHE Bootstrapping.

Jae Hyung Ju, Jaiyoung Park, Jongmin Kim, Minsik Kang, Donghwan Kim, Jung Hee Cheon, Jung Ho Ahn

Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security, 2024

2023

NeuJeans: Private Neural Network Inference with Joint Optimization of Convolution and Bootstrapping.

Jae Hyung Ju, Jaiyoung Park, Jongmin Kim, Donghwan Kim, Jung Ho Ahn

CoRR, 2023

Toward Practical Privacy-Preserving Convolutional Neural Networks Exploiting Fully Homomorphic Encryption.

Jaiyoung Park, Donghwan Kim, Jongmin Kim, Sangpyo Kim, Wonkyung Jung, Jung Hee Cheon, Jung Ho Ahn

CoRR, 2023

HyPHEN: A Hybrid Packing Method and Optimizations for Homomorphic Encryption-Based Neural Networks.

Donghwan Kim, Jaiyoung Park, Jongmin Kim, Sangpyo Kim, Jung Ho Ahn

CoRR, 2023

SHARP: A Short-Word Hierarchical Accelerator for Robust and Practical Fully Homomorphic Encryption.

Jongmin Kim, Sangpyo Kim, Jaewan Choi, Jaiyoung Park, Donghwan Kim, Jung Ho Ahn

Proceedings of the 50th Annual International Symposium on Computer Architecture, 2023

2022

AESPA: Accuracy Preserving Low-degree Polynomial Activation for Fast Private Inference.

Jaiyoung Park, Michael Jaemin Kim, Wonkyung Jung, Jung Ho Ahn

CoRR, 2022

2020

Accelerating Number Theoretic Transformations for Bootstrappable Homomorphic Encryption on GPUs.

Sangpyo Kim, Wonkyung Jung, Jaiyoung Park, Jung Ho Ahn

Proceedings of the IEEE International Symposium on Workload Characterization, 2020