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.

翻译：全同态加密（FHE）和零知识证明（ZKP）等密码学方案虽然提供了强大的隐私保护能力，但其计算复杂性往往成为实际应用的阻碍。多项式乘法作为这些方案中的核心运算，是主要的性能瓶颈。尽管算法进步以及GPU和FPGA等专用硬件已显示出加速此类计算的潜力，但近期人工智能加速器（TPU/NPU）的兴起带来了新的机遇。本文探讨了利用TPU/NPU加速多项式乘法，从而提升FHE和ZKP方案性能的潜力。我们提出了将多项式乘法适配于这些以AI为中心的架构的技术，并对其有效性进行了初步评估。同时，我们讨论了当前存在的局限性，并展望了进一步提升性能的未来研究方向，为先进密码学工具的广泛应用铺平道路。