BTS: An Accelerator for Bootstrappable Fully Homomorphic Encryption

Abstract

Homomorphic encryption (HE) enables the secure offloading of computations to the cloud by providing computation on encrypted data (ciphertexts). HE is based on noisy encryption schemes in which noise accumulates as more computations are applied to the data. The limited number of operations applicable to the data prevents practical applications from exploiting HE. Bootstrapping enables an unlimited number of operations or fully HE (FHE) by refreshing the ciphertext. Unfortunately, bootstrapping requires a significant amount of additional computation and memory bandwidth as well. Prior works have proposed hardware accelerators for computation primitives of FHE. However, to the best of our knowledge, this is the first to propose a hardware FHE accelerator that supports bootstrapping as a first-class citizen. In particular, we propose BTS - Bootstrappable, Technologydriven, Secure accelerator architecture for FHE. We identify the challenges of supporting bootstrapping in the accelerator and analyze the off-chip memory bandwidth and computation required. In particular, given the limitations of modern memory technology, we identify the HE parameter sets that are efficient for FHE acceleration. Based on the insights gained from our analysis, we propose BTS, which effectively exploits the parallelism innate in HE operations by arranging a massive number of processing elements in a grid. We present the design and microarchitecture of BTS, including a network-on-chip design that exploits a deterministic communication pattern. BTS shows 5,556x and 1,306x improved execution time on ResNet-20 and logistic regression over a CPU, with a chip area of 373.6mm^2 and up to 163.2W of power.

arXiv comment15 pages, 10 figures

References in corpus (4)

• EVA: An Encrypted Vector Arithmetic Language and Compiler for Efficient Homomorphic Computation
• PrivFT: Private and Fast Text Classification with Homomorphic Encryption
• HEAAN Demystified: Accelerating Fully Homomorphic Encryption Through Architecture-centric Analysis and Optimization
• Accelerating Number Theoretic Transformations for Bootstrappable Homomorphic Encryption on GPUs

Cited by in corpus (11)

• ARK: Fully Homomorphic Encryption Accelerator with Runtime Data Generation and Inter-Operation Key Reuse
• SoK: Fully Homomorphic Encryption Accelerators
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• HyPHEN: A Hybrid Packing Method and Optimizations for Homomorphic Encryption-Based Neural Networks
• NeuJeans: Private Neural Network Inference with Joint Optimization of Convolution and FHE Bootstrapping
• SZKP: A Scalable Accelerator Architecture for Zero-Knowledge Proofs
• Characterizing and Optimizing End-to-End Systems for Private Inference
• Need for zkSpeed: Accelerating HyperPlonk for Zero-Knowledge Proofs
• Towards Fast and Scalable Private Inference
• Cheddar: A Swift Fully Homomorphic Encryption Library Designed for GPU Architectures
• Leveraging ASIC AI Chips for Homomorphic Encryption