THE QUANTUM INTELLIGENCER

LLM-Driven Hardware Acceleration of FALCON: A Co-Design Approach for Post-Quantum Cryptography on FPGAs In Plain English: As quantum computers get stronger, they could break today’s online security systems. To prevent this, new types of digital locks, called post-quantum cryptography, are being developed—but they’re slow and hard to build efficiently. This research uses smart AI models to help design faster hardware for one of these new security systems, called FALCON. The AI helps create custom computer chips that speed up the security process, making it up to 2.6 times faster in key parts. This matters because it could help protect future internet communications more quickly and with less human effort in design. Summary: This paper presents a novel framework leveraging Large Language Models (LLMs) to accelerate the hardware-software co-design of post-quantum cryptography (PQC) systems, with a focus on the FALCON digital signature scheme. Given the computational intensity and implementation complexity of PQC algorithms, the authors propose using LLMs to analyze algorithmic components, identify performance bottlenecks, and generate optimized hardware descriptions for FPGA deployment. The framework enables rapid iteration and automation in accelerator design, reducing development time and effort. Quantitative evaluation shows that LLM-generated FPGA accelerators for critical kernels in FALCON achieve up to a 2.6x speedup over conventional High-Level Synthesis (HLS) approaches, with shorter critical paths and improved timing performance. However, these gains come with increased resource utilization and power consumption, indicating important design trade-offs. The study demonstrates the feasibility and advantages of integrating LLMs into the hardware design pipeline, particularly for adaptive and rapid development of PQC systems in anticipation of evolving standards and threats. Key Points: - Large Language Models (LLMs) are used to automate and accelerate the FPGA-based hardware design of post-quantum cryptographic algorithms. The focus is on the FALCON signature scheme, which is computationally intensive and challenging to optimize. The LLM analyzes the algorithm, identifies critical performance components, and generates synthesizable hardware descriptions. Human-guided LLM outputs are compared with traditional HLS methods, showing up to 2.6x speedup in kernel execution time. LLM-driven designs exhibit shorter critical paths but higher resource and power costs. The approach reduces manual design effort and enables rapid prototyping for evolving PQC standards. This represents one of the first empirical demonstrations of LLMs in hardware co-design for cryptography. Notable Quotes: - "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." Data Points: - The study presents up to a 2.6x speedup in kernel execution time using LLM-generated FPGA accelerators compared to HLS-based designs. The designs show shorter critical paths, indicating better timing performance. Resource utilization and power consumption are noted to be higher in LLM-driven implementations. The work focuses on the FALCON digital signature scheme, a finalist in NIST's PQC standardization process. Controversial Claims: - The claim that LLMs can effectively replace or significantly augment traditional high-level synthesis (HLS) workflows in hardware design may be considered bold, as it challenges established EDA practices. The assertion that human-in-the-loop LLM-generated designs outperform conventional methods by 2.6x in speed for critical kernels implies a major shift in design methodology, which may require broader validation across different algorithms and hardware platforms. Additionally, the framing of LLMs as a solution for 'rapid and adaptive' PQC design assumes sufficient reliability and correctness in AI-generated hardware code, which remains a concern in safety- and security-critical contexts. Technical Terms: - Post-Quantum Cryptography (PQC), FALCON, Large Language Models (LLMs), Hardware-Software Co-Design, Field-Programmable Gate Arrays (FPGAs), High-Level Synthesis (HLS), Critical Path, FPGA Accelerator, Digital Signature Scheme, Algorithmic Bottlenecks, Human-in-the-Loop Design, Design Automation, Electronic Design Automation (EDA) —Ada H. Pemberley Dispatch from The Prepared E0