# FAB: An FPGA-based Accelerator for Bootstrappable Fully Homomorphic Encryption Source: [FAB: An FPGA-based Accelerator for Bootstrappable Fully Homomorphic Encryption]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:25:36.384Z Authors: Rashmi Agrawal, Leo de Castro, Guowei Yang, Chiraag Juvekar, Rabia Yazicigil, Anantha Chandrakasan, Vinod Vaikuntanathan, Ajay Joshi Published: 2022-07-25T01:46:46.000Z ## Abstract FHE offers protection to private data on third-party cloud servers by allowing computations on the data in encrypted form. However, to support general-purpose encrypted computations, all existing FHE schemes require an expensive operation known as bootstrapping. Unfortunately, the computation cost and the memory bandwidth required for bootstrapping add significant overhead to FHE-based computations, limiting the practical use of FHE. In this work, we propose FAB, an FPGA-based accelerator for bootstrappable FHE. Prior FPGA-based FHE accelerators have proposed hardware acceleration of basic FHE primitives for impractical parameter sets without support for bootstrapping. FAB, for the first time ever, accelerates bootstrapping (along with basic FHE primitives) on an FPGA for a secure and practical parameter set. The key contribution of our work is to architect a balanced FAB design, which is not memory bound. To this end, we leverage recent algorithms for bootstrapping while being cognizant of the compute and memory constraints of our FPGA. We use a minimal number of functional units for computing, operate at a low frequency, leverage high data rates to and from main memory, utilize the limited on-chip memory effectively, and perform operation scheduling carefully. For bootstrapping a fully-packed ciphertext, while operating at 300 MHz, FAB outperforms existing state-of-the-art CPU and GPU implementations by 213x and 1.5x respectively. Our target FHE application is training a logistic regression model over encrypted data. For logistic regression model training scaled to 8 FPGAs on the cloud, FAB outperforms a CPU and GPU by 456x and 6.5x and provides competitive performance when compared to the state-of-the-art ASIC design at a fraction of the cost. ## 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. - [Homomorphic Encryption Kernel Latency on HE Kernels N=2^16](): Homomorphic Encryption Kernel Latency; HE Kernels N=2^16; HE Add Latency (μs): 40. ## Original sources and implementation [Original paper]() [PDF]()