Naifeng Zhang

According to our database, Naifeng Zhang authored at least 16 papers between 2020 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Accelerating Pinned Insect Specimen Digitization: A Deep Learning Pipeline for Future Collaborative Robots.

Naifeng Zhang, Arianna Salili-James, Sanson T. S. Poon, Jack Hollister, Ben Scott

Adv. Intell. Syst., January, 2026

Faster Game Solving via Hyperparameter Schedules.

Naifeng Zhang, Stephen Marcus McAleer, Tuomas Sandholm

Proceedings of the Fortieth AAAI Conference on Artificial Intelligence, 2026

2025

Towards Semantics Lifting for Scientific Computing: A Case Study on FFT.

Naifeng Zhang, Sanil Rao, Mike Franusich, Franz Franchetti

CoRR, January, 2025

A RISC-V Vector Extension for Multi-word Arithmetic.

Yunhao Lan, Larry Tang, Naifeng Zhang, Youngjin Eum, James C. Hoe, Franz Franchetti

Proceedings of the SC '25 Workshops of the International Conference for High Performance Computing, 2025

Towards Closing the Performance Gap for Cryptographic Kernels Between CPUs and Specialized Hardware.

Naifeng Zhang, Sophia Fu, Franz Franchetti

Proceedings of the 58th IEEE/ACM International Symposium on Microarchitecture, 2025

An Improved Sliding Model Control with a Novel Reaching Law for PMSM Enhancing Low-Speed Stability.

Kaikai Guo, Naifeng Zhang, Zaixin Song, Yongtao Liang, Xiangyang Wu

Proceedings of the IEEE Industry Applications Society Annual Meeting, 2025

Towards Automated Reasoning Chains for Verification of LLM-Generated Scientific Code.

Quentin Oschatz, Naifeng Zhang, Mike Franusich, Franz Franchetti

Proceedings of the IEEE High Performance Extreme Computing Conference, 2025

Code Generation for Cryptographic Kernels using Multi-word Modular Arithmetic on GPU.

Naifeng Zhang, Franz Franchetti

Proceedings of the 23rd ACM/IEEE International Symposium on Code Generation and Optimization, 2025

2023

TREBUCHET: Fully Homomorphic Encryption Accelerator for Deep Computation.

David Bruce Cousins, Yuriy Polyakov, Ahmad Al Badawi, Matthew French, Andrew G. Schmidt, Ajey P. Jacob, Benedict Reynwar, Kellie Canida, Akhilesh R. Jaiswal, Clynn Mathew, Homer Gamil, Negar Neda, Deepraj Soni, Michail Maniatakos, Brandon Reagen, Naifeng Zhang, Franz Franchetti, Patrick Brinich, Jeremy Johnson, Patrick Broderick, Mike Franusich, Bo Zhang, Zeming Cheng, Massoud Pedram

IACR Cryptol. ePrint Arch., 2023

RPU: The Ring Processing Unit.

Deepraj Soni, Negar Neda, Naifeng Zhang, Benedict Reynwar, Homer Gamil, Benjamin Heyman, Mohammed Nabeel, Ahmad Al Badawi, Yuriy Polyakov, Kellie Canida, Massoud Pedram, Michail Maniatakos, David Bruce Cousins, Franz Franchetti, Matthew French, Andrew G. Schmidt, Brandon Reagen

Proceedings of the IEEE International Symposium on Performance Analysis of Systems and Software, 2023

Generating High-Performance Number Theoretic Transform Implementations for Vector Architectures.

Naifeng Zhang, Austin Ebel, Negar Neda, Patrick Brinich, Benedict Reynwar, Andrew G. Schmidt, Mike Franusich, Jeremy Johnson, Brandon Reagen, Franz Franchetti

Proceedings of the IEEE High Performance Extreme Computing Conference, 2023

Optimization and Performance Analysis of Shor's Algorithm in Qiskit.

Dewang Sun, Naifeng Zhang, Franz Franchetti

Proceedings of the IEEE High Performance Extreme Computing Conference, 2023

2022

Towards the shortest DRAT proof of the Pigeonhole Principle.

Isaac Grosof, Naifeng Zhang, Marijn J. H. Heule

CoRR, 2022

2021

GenMAT: A General-Purpose Machine Learning-Driven Auto-Tuner for Heterogeneous Platforms.

Naifeng Zhang, Ajitesh Srivastava, Rajgopal Kannan, Viktor K. Prasanna

Proceedings of the IEEE/ACM Programming Environments for Heterogeneous Computing, 2021

2020

Compiler Abstractions and Runtime for Extreme-scale SAR and CFD Workloads.

Connor Imes, Alexei Colin, Naifeng Zhang, Ajitesh Srivastava, Viktor K. Prasanna, John Paul Walters

Proceedings of the 5th IEEE/ACM International Workshop on Extreme Scale Programming Models and Middleware, 2020

Towards High Performance, Portability, and Productivity: Lightweight Augmented Neural Networks for Performance Prediction.

Ajitesh Srivastava, Naifeng Zhang, Rajgopal Kannan, Viktor K. Prasanna

Proceedings of the 27th IEEE International Conference on High Performance Computing, 2020