# Adapting AlphaEvolve to Optimize Fully Homomorphic Encryption on TPUs Source: [Adapting AlphaEvolve to Optimize Fully Homomorphic Encryption on TPUs]() Collection index: [llms.txt]() Data updated: 2026-05-16T05:00:12.406Z Authors: Shruthi Gorantala, Jianming Tong, Asra Ali, Baiyu Li, Jonathan Katz, Jeremy Kun, Thomas Steinke, Abhradeep Thakurta, Julian Walker, Amir Yazdanbakhsh Published: 2026-05-14T11:39:04.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Bootstrap on TFHE jaxite](): Bootstrap; TFHE jaxite; Latency (ms): 4. - [Blind Rotation on TFHE jaxite](): Blind Rotation; TFHE jaxite; Latency (ms): 3.5. - [CKKS Multiplication (CKKS-Mult) on CKKS CROSS](): CKKS Multiplication (CKKS-Mult); CKKS CROSS; Latency (µs): 5359. - [CKKS Rotation (CKKS-Rot) on CKKS CROSS](): CKKS Rotation (CKKS-Rot); CKKS CROSS; Latency (µs): 3714. ## Original sources and implementation [Original paper]() [PDF]()