4 citations · 4 across the 8 of their papers we have counts for
8 papers
Towards Closing the Performance Gap for Cryptographic Kernels Between CPUs and Specialized Hardware
Naifeng Zhang, Sophia Fu, Franz Franchetti
Specialized hardware like application-specific integrated circuits (ASICs) remains the primary accelerator type for cryptographic kernels based on large integer arithmetic. Prior w…
Towards Semantics Lifting for Scientific Computing: A Case Study on FFT
Naifeng Zhang, Sanil Rao, Mike Franusich +1
The rise of automated code generation tools, such as large language models (LLMs), has introduced new challenges in ensuring the correctness and efficiency of scientific software,…
Code Generation for Cryptographic Kernels using Multi-word Modular Arithmetic on GPU
Naifeng Zhang, Franz Franchetti
Fully homomorphic encryption (FHE) and zero-knowledge proofs (ZKPs) are emerging as solutions for data security in distributed environments. However, the widespread adoption of the…
Faster Game Solving via Hyperparameter Schedules
Naifeng Zhang, Stephen McAleer, Tuomas Sandholm
Counterfactual regret minimization (CFR) algorithms are a foundational class of methods for solving imperfect-information games, with the time average of their iterates converging…
TREBUCHET: Fully Homomorphic Encryption Accelerator for Deep Computation
David Bruce Cousins, Yuriy Polyakov, Ahmad Al Badawi +21
Secure computation is of critical importance to not only the DoD, but across financial institutions, healthcare, and anywhere personally identifiable information (PII) is accessed.…
RPU: The Ring Processing Unit
Deepraj Soni, Negar Neda, Naifeng Zhang +14
Ring-Learning-with-Errors (RLWE) has emerged as the foundation of many important techniques for improving security and privacy, including homomorphic encryption and post-quantum cr…
Towards the shortest DRAT proof of the Pigeonhole Principle
Isaac Grosof, Naifeng Zhang, Marijn J. H. Heule
The Pigeonhole Principle (PHP) has been heavily studied in automated reasoning, both theoretically and in practice. Most solvers have exponential runtime and proof length, while so…
Towards High Performance, Portability, and Productivity: Lightweight Augmented Neural Networks for Performance Prediction
Ajitesh Srivastava, Naifeng Zhang, Rajgopal Kannan +1
Writing high-performance code requires significant expertise in the programming language, compiler optimizations, and hardware knowledge. This often leads to poor productivity and…