Adapting AlphaEvolve to Optimize Fully Homomorphic Encryption on TPUs

• URL: http://arxiv.org/abs/2605.14718v1
• Date: Thu, 14 May 2026 11:39:04 GMT
• Title: Adapting AlphaEvolve to Optimize Fully Homomorphic Encryption on TPUs
• Abstract summary: We use AlphaEvolve to automate the exploration of hardware-aware cryptographic- Kernel optimizations.<n>We evaluate AlphaEvolve optimization on primitives for both the TFHE (Jaxite) and CKKS (CROSS) FHE schemes on Google Cloud TPUv5e.<n>Within 24 hours of automated exploration, AlphaEvolve discovered implementation-level optimizations that improve TFHE bootstrap latency by 2.5x and CKKS rotation and multiplication latency by 1.31x and 1.18x, respectively.
• Score: 22.823899868762364
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The deployment of Fully Homomorphic Encryption (FHE) at scale is hindered due to its heavy computational overhead. While specialized hardware accelerators like Google Tensor Processing Units (TPUs) can help, mapping complex cryptographic kernels onto such architectures remains a challenge. Efficient execution requires co-optimization between the systolic array-based Matrix Multiplication Unit (MXU) and Vector Processing Units (VPUs), as well as the orchestration of data movement across the vector register files. Existing compiler stacks often abstract low-level hardware utilization, requiring developers to adopt a manual trial-and-error process that often results in fragmented execution and underutilized resources. To accelerate this development process, we use AlphaEvolve to automate the exploration of hardware-aware cryptographic-kernel optimizations. We frame optimization as an evolutionary search problem, utilizing the closed-loop system provided by AlphaEvolve, that leverages LLM-driven code generation. We use real-world feedback from hardware execution and rigorous correctness testing to guide the evolution process. We evaluate AlphaEvolve optimization on primitives for both the TFHE (Jaxite) and CKKS (CROSS) FHE schemes on Google Cloud TPUv5e, a contemporary TPU architecture. Within 24 hours of automated exploration, AlphaEvolve discovered implementation-level optimizations that improve TFHE bootstrap latency by 2.5x and CKKS rotation and multiplication latency by 1.31x and 1.18x, respectively, relative to human-engineered state of the art. These results demonstrate that AlphaEvolve can be used to enable researchers to navigate the optimization trade-offs between cryptography, compilers, and hardware accelerators.

Related papers

• AutoVecCoder: Teaching LLMs to Generate Explicitly Vectorized Code [69.84692701646769]
  AutoVecCoder is a novel framework designed to empower Large Language Models with the capability of automated explicit vectorization.<n>VecPrompt, an automated data synthesis pipeline, injects domain-specific intrinsic knowledge.<n>VecRL, a reinforcement learning framework, aligns code generation with execution efficiency.<n>AutoVecCoder-8B trained by this framework achieves state-of-the-art performance on the SSE and AVX subsets of SimdBench.
  arXiv  Detail & Related papers  (2026-05-18T07:33:15Z)
• Making LLMs Optimize Multi-Scenario CUDA Kernels Like Experts [25.264078143000045]
  Current automated kernel optimization methods focus on machine learning applications, while overlooking broader domains like sparse matrix operations in scientific computing.<n>In this paper, we address the absence of systematic evaluation for multi-scenario settings introducing MSBench.<n>Building on this benchmark, we introduce MSMaster, a hardware-aware system for kernel optimization that leverages GPU profiling information and automatically constructs the full compilation and execution toolchain.
  arXiv  Detail & Related papers  (2026-03-07T12:22:43Z)
• K-Search: LLM Kernel Generation via Co-Evolving Intrinsic World Model [57.440609834690385]
  Existing approaches treat Large Language Models (LLMs) as rapid code generators within evolutionary loops.<n>We propose Search via Co-Evolving World Model and build K-Search based on this method.<n>We evaluate K-Search on diverse, complex kernels FlashInfer, including GQA, MLA, and MoE kernels.
  arXiv  Detail & Related papers  (2026-02-22T11:06:22Z)
• PEAK: A Performance Engineering AI-Assistant for GPU Kernels Powered by Natural Language Transformations [0.8699280339422538]
  We introduce PEAK, a Performance Engineering AI-Assistant for Kernels powered by natural language transformations.<n>We show that our implementations are competitive with vendor libraries when available, and for HLSL (without a library) our documented FLOPS.
  arXiv  Detail & Related papers  (2025-12-22T04:15:24Z)
• AlphaEvolve: A coding agent for scientific and algorithmic discovery [63.13852052551106]
  We present AlphaEvolve, an evolutionary coding agent that substantially enhances capabilities of state-of-the-art LLMs.<n>AlphaEvolve orchestrates an autonomous pipeline of LLMs, whose task is to improve an algorithm by making direct changes to the code.<n>We demonstrate the broad applicability of this approach by applying it to a number of important computational problems.
  arXiv  Detail & Related papers  (2025-06-16T06:37:18Z)
• Leveraging ASIC AI Chips for Homomorphic Encryption [12.209134343914537]
  homomorphic encryption (HE) offers strong privacy guarantee, but it requires substantially more resources than computing on plaintext.<n> accelerators have emerged to mitigate this latency issue, but with the high cost of ASICs.<n>We show that HE primitives can be converted to AI operators and accelerated on existing ASIC AI accelerators, like TPUs, which are already widely deployed in the cloud.
  arXiv  Detail & Related papers  (2025-01-13T04:08:14Z)
• Harnessing Deep Learning and HPC Kernels via High-Level Loop and Tensor Abstractions on CPU Architectures [67.47328776279204]
  This work introduces a framework to develop efficient, portable Deep Learning and High Performance Computing kernels. We decompose the kernel development in two steps: 1) Expressing the computational core using Processing Primitives (TPPs) and 2) Expressing the logical loops around TPPs in a high-level, declarative fashion. We demonstrate the efficacy of our approach using standalone kernels and end-to-end workloads that outperform state-of-the-art implementations on diverse CPU platforms.
  arXiv  Detail & Related papers  (2023-04-25T05:04:44Z)
• evosax: JAX-based Evolution Strategies [0.0]
  We release evosax: a JAX-based library of evolutionary optimization algorithms. evosax implements 30 evolutionary optimization algorithms including finite-difference-based, estimation-of-distribution evolution strategies and various genetic algorithms. It is designed in a modular fashion and allows for flexible usage via a simple ask-evaluate-tell API.
  arXiv  Detail & Related papers  (2022-12-08T10:34:42Z)
• PolyDL: Polyhedral Optimizations for Creation of High Performance DL primitives [55.79741270235602]
  We present compiler algorithms to automatically generate high performance implementations of Deep Learning primitives. We develop novel data reuse analysis algorithms using the polyhedral model. We also show that such a hybrid compiler plus a minimal library-use approach results in state-of-the-art performance.
  arXiv  Detail & Related papers  (2020-06-02T06:44:09Z)
• PolyScientist: Automatic Loop Transformations Combined with Microkernels for Optimization of Deep Learning Primitives [55.79741270235602]
  We develop a hybrid solution to the development of deep learning kernels. We use the advanced polyhedral technology to automatically tune the outer loops for performance.
  arXiv  Detail & Related papers  (2020-02-06T08:02:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.