Towards a Functionally Complete and Parameterizable TFHE Processor

2025-10-27

AI Summary

TFHE offers optimal bootstrapping performance and support for arbitrary function evaluation, but its homomorphic circuit evaluation incurs prohibitively high computational overhead—reducing throughput by several orders of magnitude compared to plaintext computation—severely limiting practical deployment. To address this, we propose a programmable FPGA-based hardware accelerator tailored for TFHE, implementing a dedicated processor architecture that operates entirely within the encrypted domain with full instruction support. Our key innovations include a parameterized, programmable bootstrapping unit that boosts bootstrapping throughput by 240–480%, alongside optimized memory bandwidth utilization and pipelined circuit execution to accelerate both linear and nonlinear homomorphic operations. The resulting design is compact, scalable, and delivers significantly improved homomorphic computation throughput. This work establishes a viable architectural foundation for fully FPGA-deployed TFHE systems.

Technology Category

• Machine Learning: Hardware-aware ML
• Search and Optimization: Heuristic Search
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Security and Privacy: Privacy-enhancing technologies
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Search and Retrieval-Augmented AI: Agentic search

Abstract

Fully homomorphic encryption allows the evaluation of arbitrary functions on encrypted data. It can be leveraged to secure outsourced and multiparty computation. TFHE is a fast torus-based fully homomorphic encryption scheme that allows both linear operations, as well as the evaluation of arbitrary non-linear functions. It currently provides the fastest bootstrapping operation performance of any other FHE scheme. Despite its fast performance, TFHE suffers from a considerably higher computational overhead for the evaluation of homomorphic circuits. Computations in the encrypted domain are orders of magnitude slower than their unencrypted equivalents. This bottleneck hinders the widespread adoption of (T)FHE for the protection of sensitive data. While state-of-the-art implementations focused on accelerating and outsourcing single operations, their scalability and practicality are constrained by high memory bandwidth costs. In order to overcome this, we propose an FPGA-based hardware accelerator for the evaluation of homomorphic circuits. Specifically, we design a functionally complete TFHE processor for FPGA hardware capable of processing instructions on the data completely on the FPGA. In order to achieve a higher throughput from our TFHE processor, we implement an improved programmable bootstrapping module which outperforms the current state-of-the-art by 240% to 480% more bootstrappings per second. Our efficient, compact, and scalable design lays the foundation for implementing complete FPGA-based TFHE processor architectures.

Problem

• Accelerating TFHE homomorphic circuit evaluation performance
• Reducing computational overhead for encrypted data processing
• Overcoming memory bandwidth constraints in FHE implementations

Innovation

• FPGA-based hardware accelerator for homomorphic circuits
• Functionally complete TFHE processor for FPGA
• Improved programmable bootstrapping module for higher throughput