BTS: An Accelerator for Bootstrappable Fully Homomorphic Encryption
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Abstract (arXiv)
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.
Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). First posted on arXiv 31 Dec 2021.
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Citing papers per year: 2022: 2; 2023: 5; 2024: 1; 2025: 1; 2026: 3.
12 papers.
- 2023HyPHEN: A Hybrid Packing Method and Optimizations for Homomorphic Encryption-Based Neural Networkscited by 9
- 2023NeuJeans: Private Neural Network Inference with Joint Optimization of Convolution and FHE Bootstrappingcited by 6
- 2023GPU-based Private Information Retrieval for On-Device Machine Learning Inferencecited by 5
- 2022MPC-Pipe: an Efficient Pipeline Scheme for Secure Multi-party Machine Learning Inferencecited by 4
- 2025Leveraging ASIC AI Chips for Homomorphic Encryptioncited by 3
- 2023Approximating ReLU on a Reduced Ring for Efficient MPC-based Private Inferencecited by 2
- 2024From Algorithm to Hardware: A Survey on Efficient and Safe Deployment of Deep Neural Networkscited by 2
- 2026AEGIS: Scaling Long-Sequence Homomorphic Encrypted Transformer Inference via Hybrid Parallelism on Multi-GPU Systemscited by 2
- 2023Towards Fast and Scalable Private Inferencecited by 1
- 2022Revealing Secrets From Pre-trained Modelscited by 0
- 2026FEnc2: Unifying Data Packing for Efficient Private Inference via Convolution and Architecture-Aware Fragment Encodingcited by 0
- 2026OptiPrime: Optimizing Private Inference through Protocol-Hardware Co-designcited by 0
- 2026OptiPrime: Optimizing Private Inference through Protocol-Hardware Co-design15 Sep 2026
- 2026FEnc2: Unifying Data Packing for Efficient Private Inference via Convolution and Architecture-Aware Fragment Encoding15 Jun 2026
- 2026AEGIS: Scaling Long-Sequence Homomorphic Encrypted Transformer Inference via Hybrid Parallelism on Multi-GPU Systems3 Apr 2026
- 2025Leveraging ASIC AI Chips for Homomorphic Encryption13 Jan 2025
- 2024From Algorithm to Hardware: A Survey on Efficient and Safe Deployment of Deep Neural Networks9 May 2024
- 2023NeuJeans: Private Neural Network Inference with Joint Optimization of Convolution and FHE Bootstrapping7 Dec 2023
- 2023Approximating ReLU on a Reduced Ring for Efficient MPC-based Private Inference9 Sep 2023
- 2023Towards Fast and Scalable Private Inference9 Jul 2023
- 2023HyPHEN: A Hybrid Packing Method and Optimizations for Homomorphic Encryption-Based Neural Networks5 Feb 2023
- 2023GPU-based Private Information Retrieval for On-Device Machine Learning Inference26 Jan 2023
- 2022MPC-Pipe: an Efficient Pipeline Scheme for Secure Multi-party Machine Learning Inference27 Sep 2022
- 2022Revealing Secrets From Pre-trained Models19 Jul 2022