mlsanityData to 7 Oct 2026

F1: A Fast and Programmable Accelerator for Fully Homomorphic Encryption (Extended Version)

arXiv 2109.05371 ↗First posted 11 Sep 2021cs.CR cs.ARcited by 23

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Abstract (arXiv)

Fully Homomorphic Encryption (FHE) allows computing on encrypted data, enabling secure offloading of computation to untrusted serves. Though it provides ideal security, FHE is expensive when executed in software, 4 to 5 orders of magnitude slower than computing on unencrypted data. These overheads are a major barrier to FHE's widespread adoption. We present F1, the first FHE accelerator that is programmable, i.e., capable of executing full FHE programs. F1 builds on an in-depth architectural analysis of the characteristics of FHE computations that reveals acceleration opportunities. F1 is a wide-vector processor with novel functional units deeply specialized to FHE primitives, such as modular arithmetic, number-theoretic transforms, and structured permutations. This organization provides so much compute throughput that data movement becomes the bottleneck. Thus, F1 is primarily designed to minimize data movement. The F1 hardware provides an explicitly managed memory hierarchy and mechanisms to decouple data movement from execution. A novel compiler leverages these mechanisms to maximize reuse and schedule off-chip and on-chip data movement. We evaluate F1 using cycle-accurate simulations and RTL synthesis. F1 is the first system to accelerate complete FHE programs and outperforms state-of-the-art software implementations by gmean 5400x and by up to 17000x. These speedups counter most of FHE's overheads and enable new applications, like real-time private deep learning in the cloud.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Sep 2021.

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Citing papers per year: 2022: 3; 2023: 5; 2024: 5; 2025: 5; 2026: 5.

23 papers.

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  1. 2022On the Robustness of Dataset Inferencecited by 8
  2. 2023GPU-based Private Information Retrieval for On-Device Machine Learning Inferencecited by 5
  3. 2024Privacy-Preserving Diffusion Model Using Homomorphic Encryptioncited by 4
  4. 2024CryptoTrain: Fast Secure Training on Encrypted Datasetcited by 3
  5. 2025Leveraging ASIC AI Chips for Homomorphic Encryptioncited by 3
  6. 2023Considerations on the Theory of Training Models with Differential Privacycited by 2
  7. 2023Approximating ReLU on a Reduced Ring for Efficient MPC-based Private Inferencecited by 2
  8. 2023Hyperdimensional Computing as a Rescue for Efficient Privacy-Preserving Machine Learning-as-a-Servicecited by 2
  9. 2025A Scalable Multi-GPU Framework for Encrypted Large-Model Inferencecited by 2
  10. 2026AEGIS: Scaling Long-Sequence Homomorphic Encrypted Transformer Inference via Hybrid Parallelism on Multi-GPU Systemscited by 2
  11. 2023Towards Fast and Scalable Private Inferencecited by 1
  12. 2024PackVFL: Efficient HE Packing for Vertical Federated Learningcited by 1
11 more
  1. 2024VeriSplit: Secure and Practical Offloading of Machine Learning Inferences across IoT Devicescited by 1
  2. 2025Efficient Privacy-Preserving Recommendation on Sparse Data using Fully Homomorphic Encryptioncited by 1
  3. 2025CrypTorch: PyTorch-based Auto-tuning Compiler for Machine Learning with Multi-party Computationcited by 1
  4. 2022One Picture is Worth a Thousand Words: A New Wallet Recovery Processcited by 0
  5. 2022Revealing Secrets From Pre-trained Modelscited by 0
  6. 2024TensorTEE: Unifying Heterogeneous TEE Granularity for Efficient Secure Collaborative Tensor Computingcited by 0
  7. 2025Securing Transfer-Learned Networks with Reverse Homomorphic Encryptioncited by 0
  8. 2026A3D: Agentic AI flow for autonomous Accelerator Designcited by 0
  9. 2026FEnc2: Unifying Data Packing for Efficient Private Inference via Convolution and Architecture-Aware Fragment Encodingcited by 0
  10. 2026Homomorphic Encryptions for Privacy Preserving Visioncited by 0
  11. 2026OptiPrime: Optimizing Private Inference through Protocol-Hardware Co-designcited by 0