Accelerating Post-Quantum Cryptography via LLM-Driven Hardware-Software Co-Design
This addresses the challenge of efficiently securing data against quantum threats for cryptography and hardware design communities, representing a novel method for a known bottleneck.
The paper tackled the computational complexity of implementing post-quantum cryptography (PQC) by using Large Language Models (LLMs) to accelerate hardware-software co-design for the FALCON scheme, achieving up to 2.6x speedup in kernel execution time with shorter critical paths.
Post-quantum cryptography (PQC) is crucial for securing data against emerging quantum threats. However, its algorithms are computationally complex and difficult to implement efficiently on hardware. In this paper, we explore the potential of Large Language Models (LLMs) to accelerate the hardware-software co-design process for PQC, with a focus on the FALCON digital signature scheme. We present a novel framework that leverages LLMs to analyze PQC algorithms, identify performance-critical components, and generate candidate hardware descriptions for FPGA implementation. We present the first quantitative comparison between LLM-driven synthesis and conventional HLS-based approaches for low-level compute-intensive kernels in FALCON, showing that human-in-the-loop LLM-generated accelerators can achieve up to 2.6x speedup in kernel execution time with shorter critical paths, while highlighting trade-offs in resource utilization and power consumption. Our results suggest that LLMs can minimize design effort and development time by automating FPGA accelerator design iterations for PQC algorithms, offering a promising new direction for rapid and adaptive PQC accelerator design on FPGAs.