TPU as Cryptographic Accelerator
| Autor: | Karanjai, Rabimba, Shin, Sangwon, Xiong, and Wujie, Fan, Xinxin, Chen, Lin, Zhang, Tianwei, Suh, Taeweon, Shi, Weidong, Kuchta, Veronika, Sica, Francesco, Xu, Lei |
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| Rok vydání: | 2023 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1145/3696843.3696844 |
| Popis: | Cryptographic schemes like Fully Homomorphic Encryption (FHE) and Zero-Knowledge Proofs (ZKPs), while offering powerful privacy-preserving capabilities, are often hindered by their computational complexity. Polynomial multiplication, a core operation in these schemes, is a major performance bottleneck. While algorithmic advancements and specialized hardware like GPUs and FPGAs have shown promise in accelerating these computations, the recent surge in AI accelerators (TPUs/NPUs) presents a new opportunity. This paper explores the potential of leveraging TPUs/NPUs to accelerate polynomial multiplication, thereby enhancing the performance of FHE and ZKP schemes. We present techniques to adapt polynomial multiplication to these AI-centric architectures and provide a preliminary evaluation of their effectiveness. We also discuss current limitations and outline future directions for further performance improvements, paving the way for wider adoption of advanced cryptographic tools. Comment: Accepted to be presented in HASP 24, as part of MICRO 2024 |
| Databáze: | arXiv |
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