FPGA Accelerator Boosts TFHE Bootstrapping Performance by Up to 480%

Global: FPGA Accelerator Boosts TFHE Bootstrapping Performance by Up to 480%

The authors of a recent arXiv preprint (arXiv:2510.23483v2) announced a field‑programmable gate array (FPGA)‑based accelerator designed to improve the efficiency of the TFHE fully homomorphic encryption scheme. The paper, posted in October 2025, reports that the new processor can execute 240 % to 480 % more bootstrapping operations per second than existing implementations, addressing a primary bottleneck in homomorphic computation.

Background on TFHE

Fully homomorphic encryption (FHE) enables computation on encrypted data without decryption, a capability that underpins secure outsourced and multiparty processing. The TFHE scheme, built on torus‑based mathematics, is recognized for its fast bootstrapping but suffers from high computational overhead, making practical deployment challenging.

Design of the FPGA Processor

The proposed architecture implements a functionally complete TFHE processor entirely on FPGA fabric, allowing instructions to be processed without off‑chip data transfers. By integrating arithmetic units, memory buffers, and control logic on a single device, the design aims to reduce memory‑bandwidth constraints that limit existing software‑centric solutions.

Programmable Bootstrapping Module

A central innovation is an improved programmable bootstrapping module. According to the authors, the module leverages parallelized number‑theoretic transforms and custom routing to achieve the reported 240 %–480 % increase in bootstrapping throughput, surpassing the prior state‑of‑the‑art.

Performance Evaluation

Benchmarking against leading TFHE implementations shows a marked reduction in latency for both linear and non‑linear homomorphic operations. The authors note that while the accelerator accelerates bootstrapping, overall circuit evaluation remains slower than plaintext computation, though the gap narrows considerably.

Implications for Secure Computation

If the performance gains translate to broader workloads, the accelerator could make TFHE‑based solutions more viable for privacy‑preserving cloud services, encrypted machine learning, and secure multi‑party analytics. Industry observers have highlighted the potential for FPGA‑based FHE accelerators to complement emerging hardware trends.

Future Directions

The study suggests further scaling through higher‑capacity FPGA devices and integration with heterogeneous compute platforms. Continued optimization of memory access patterns and support for additional FHE schemes are identified as avenues for future research.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.