TPU as Cryptographic Accelerator

TL;DR

This paper investigates using AI accelerators like TPUs to speed up polynomial multiplication in cryptographic schemes such as FHE and ZKPs, aiming to overcome computational bottlenecks and improve privacy-preserving technologies.

Contribution

It introduces methods to adapt polynomial multiplication for TPUs/NPUs and provides initial evaluations demonstrating potential performance gains.

Findings

Preliminary results show TPU acceleration improves polynomial multiplication speed.

Identifies current limitations of TPU-based cryptographic computations.

Outlines future research directions for performance enhancement.

Abstract

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.