ARK: Fully Homomorphic Encryption Accelerator with Runtime Data Generation and Inter-Operation Key Reuse
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Abstract (arXiv)
Homomorphic Encryption (HE) is one of the most promising post-quantum cryptographic schemes that enable privacy-preserving computation on servers. However, noise accumulates as we perform operations on HE-encrypted data, restricting the number of possible operations. Fully HE (FHE) removes this restriction by introducing the bootstrapping operation, which refreshes the data; however, FHE schemes are highly memory-bound. Bootstrapping, in particular, requires loading GBs of evaluation keys and plaintexts from off-chip memory, which makes FHE acceleration fundamentally bottlenecked by the off-chip memory bandwidth. In this paper, we propose ARK, an Accelerator for FHE with Runtime data generation and inter-operation Key reuse. ARK enables practical FHE workloads with a novel algorithm-architecture co-design to accelerate bootstrapping. We first eliminate the off-chip memory bandwidth bottleneck through runtime data generation and inter-operation key reuse. This approach enables ARK to fully exploit on-chip memory by substantially reducing the size of the working set. On top of such algorithmic enhancements, we build ARK microarchitecture that minimizes on-chip data movement through an efficient, alternating data distribution policy based on the data access patterns and a streamlined dataflow organization of the tailored functional units -- including base conversion, number-theoretic transform, and automorphism units. Overall, our co-design effectively handles the heavy computation and data movement overheads of FHE, drastically reducing the cost of HE operations, including bootstrapping.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 May 2022.
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Citing papers per year: 2023: 4; 2024: 1; 2025: 4; 2026: 2.
11 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
- 2025Leveraging ASIC AI Chips for Homomorphic Encryptioncited by 3
- 2023Approximating ReLU on a Reduced Ring for Efficient MPC-based Private Inferencecited by 2
- 2024HEQuant: Marrying Homomorphic Encryption and Quantization for Communication-Efficient Private Inferencecited by 2
- 2025A Scalable Multi-GPU Framework for Encrypted Large-Model Inferencecited by 2
- 2023Towards Fast and Scalable Private Inferencecited by 1
- 2025CrypTorch: PyTorch-based Auto-tuning Compiler for Machine Learning with Multi-party Computationcited by 1
- 2025EP-HDC: Hyperdimensional Computing with Encrypted Parameters for High-Throughput Privacy-Preserving Inferencecited 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
- 2025A Scalable Multi-GPU Framework for Encrypted Large-Model Inference12 Dec 2025
- 2025CrypTorch: PyTorch-based Auto-tuning Compiler for Machine Learning with Multi-party Computation24 Nov 2025
- 2025EP-HDC: Hyperdimensional Computing with Encrypted Parameters for High-Throughput Privacy-Preserving Inference1 Nov 2025
- 2025Leveraging ASIC AI Chips for Homomorphic Encryption13 Jan 2025
- 2024HEQuant: Marrying Homomorphic Encryption and Quantization for Communication-Efficient Private Inference29 Jan 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