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The article reviews two recent ISCA 2025 papers—FAST and Neo—that introduce hardware and GPU‑based accelerators employing hoisting, KLSS, and Tensor Core optimizations to significantly boost the performance of fully homomorphic encryption workloads.
At HPCA 2025, two standout papers—UniNDP, a unified compilation and simulation tool for near‑DRAM processing architectures, and WarpDrive, a GPU‑based fully homomorphic encryption accelerator leveraging Tensor and CUDA cores—demonstrate significant performance gains for AI workloads and privacy‑preserving computation.
Ant Group’s Computing Systems Lab announced that its GPU‑accelerated fully homomorphic encryption framework WarpDrive, which exploits Tensor and CUDA cores for high‑throughput NTT operations and parallel kernel designs, has been accepted as a paper at the IEEE HPCA 2025 conference.
This article explores the limitations of current non‑fully homomorphic privacy computing techniques, traces the evolution of fully homomorphic encryption, examines its practical applications in finance and machine learning, and discusses engineering challenges, protocol choices, and implementation considerations for secure data processing.