# ARK: Fully Homomorphic Encryption Accelerator with Runtime Data Generation and Inter-Operation Key Reuse

arXiv: https://arxiv.org/abs/2205.00922 · HTML: https://mlsanity.com/paper/2205.00922/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 May 2022.

## Built on by (most cited first, 12 of 12)

- [HyPHEN: A Hybrid Packing Method and Optimizations for Homomorphic Encryption-Based Neural Networks](https://mlsanity.com/paper/2302.02407/) (2023)
- [NeuJeans: Private Neural Network Inference with Joint Optimization of Convolution and FHE Bootstrapping](https://mlsanity.com/paper/2312.04356/) (2023)
- [Leveraging ASIC AI Chips for Homomorphic Encryption](https://mlsanity.com/paper/2501.07047/) (2025)
- [A Scalable Multi-GPU Framework for Encrypted Large-Model Inference](https://mlsanity.com/paper/2512.11269/) (2025)
- [Approximating ReLU on a Reduced Ring for Efficient MPC-based Private Inference](https://mlsanity.com/paper/2309.04875/) (2023)
- [HEQuant: Marrying Homomorphic Encryption and Quantization for Communication-Efficient Private Inference](https://mlsanity.com/paper/2401.15970/) (2024)
- [Towards Fast and Scalable Private Inference](https://mlsanity.com/paper/2307.04077/) (2023)
- [CrypTorch: PyTorch-based Auto-tuning Compiler for Machine Learning with Multi-party Computation](https://mlsanity.com/paper/2511.19711/) (2025)
- [EP-HDC: Hyperdimensional Computing with Encrypted Parameters for High-Throughput Privacy-Preserving Inference](https://mlsanity.com/paper/2511.00737/) (2025)
- [FEnc2: Unifying Data Packing for Efficient Private Inference via Convolution and Architecture-Aware Fragment Encoding](https://mlsanity.com/paper/2606.16359/) (2026)
- [OptiPrime: Optimizing Private Inference through Protocol-Hardware Co-design](https://mlsanity.com/paper/2609.16898/) (2026)
- [Static Bootstrap Placement for Encrypted Language Model Decoding](https://mlsanity.com/paper/2610.04912/) (2026)

## 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.