F1: A Fast and Programmable Accelerator for Fully Homomorphic Encryption (Extended Version) (2109.05371v2)

Abstract: Fully Homomorphic Encryption (FHE) allows computing on encrypted data, enabling secure offloading of computation to untrusted serves. Though it provides ideal security, FHE is expensive when executed in software, 4 to 5 orders of magnitude slower than computing on unencrypted data. These overheads are a major barrier to FHE's widespread adoption. We present F1, the first FHE accelerator that is programmable, i.e., capable of executing full FHE programs. F1 builds on an in-depth architectural analysis of the characteristics of FHE computations that reveals acceleration opportunities. F1 is a wide-vector processor with novel functional units deeply specialized to FHE primitives, such as modular arithmetic, number-theoretic transforms, and structured permutations. This organization provides so much compute throughput that data movement becomes the bottleneck. Thus, F1 is primarily designed to minimize data movement. The F1 hardware provides an explicitly managed memory hierarchy and mechanisms to decouple data movement from execution. A novel compiler leverages these mechanisms to maximize reuse and schedule off-chip and on-chip data movement. We evaluate F1 using cycle-accurate simulations and RTL synthesis. F1 is the first system to accelerate complete FHE programs and outperforms state-of-the-art software implementations by gmean 5400x and by up to 17000x. These speedups counter most of FHE's overheads and enable new applications, like real-time private deep learning in the cloud.

• The paper introduces F1, a programmable accelerator that drastically reduces FHE overhead by supporting multiple schemes such as BGV, GSW, and CKKS.
• It details a wide-vector architecture with specialized functional units that improve modular arithmetic and number-theoretic transforms performance significantly.
• The design employs optimized data movement and a co-designed compiler, achieving speedups up to 17,412× over CPU implementations.

Overview of "F1: A Fast and Programmable Accelerator for Fully Homomorphic Encryption (Extended Version)"

The paper presents F1, a hardware accelerator designed specifically for Fully Homomorphic Encryption (FHE). F1 addresses one of the central challenges in FHE: its inherent computational overheads. FHE enables computations on encrypted data, ensuring data privacy even when processed by untrusted servers. However, performance costs have so far impeded its widespread adoption.

Key Contributions

• Programmability and Flexibility: Unlike previous FHE accelerators, F1 efficiently supports full homomorphic encryption programs while being versatile enough to handle various FHE schemes like BGV, GSW, and CKKS. This is achieved through its architecture that emphasizes vector processing with highly specialized functional units tailored for FHE operations.
• Architecture Design: F1 incorporates a wide-vector architecture optimized for high-throughput processing. Its functional units are specialized for operations such as modular arithmetic and number-theoretic transforms (NTTs), offering a marked improvement over traditional FHE circuit implementations.
• Data Movement Optimization: The paper identifies data movement as a primary bottleneck in FHE computation and strategically designs F1 to minimize data movement. This is facilitated through an explicitly managed memory hierarchy and decoupled data orchestration that maximizes data reuse and minimizes off-chip data transfers.
• Compiler Strategies: A static scheduling compiler co-designed with the hardware orchestrates data movement and computation scheduling to ensure high resource utilization and application throughput.

Performance

F1's performance is evaluated through extensive experimentation using benchmarks adapted from state-of-the-art FHE applications such as logistic regression, neural network inference, and bootstrapping. Significant speedups, between 1,195× and 17,412× compared to existing CPU implementations, are demonstrated, showcasing F1's ability to mitigate FHE's computational overheads effectively. The results emphasize F1's potential to enable real-time private data processing applications, like secure deep learning inference in the cloud, which were previously infeasible due to latency constraints.

Future Implications and Developments

The successful implementation of F1 marks a substantial advancement in FHE technology. The architectural innovations, particularly emphasizing programmable flexibility and data movement efficiency, set the groundwork for further research into scalable and efficient cryptographic accelerators. As computational demands and data security concerns grow, continued exploration into FHE acceleration will likely lead to even more efficient designs capable of supporting larger datasets and more complex applications.

In conclusion, F1 represents an important step forward in the practical application of fully homomorphic encryption. The insights into architectural design and the comprehensive approach to overcoming FHE's fundamental challenges provide a strong foundation for future enhancements in secure computation technologies.