FinHardBench: Can LLMs Generate Latency-Aware Hardware for Financial Computing?

2026-08-01

AI Summary

This work presents the first systematic evaluation of large language models (LLMs) for generating latency-sensitive financial FPGA hardware, addressing the need for rapid iteration amid frequent protocol and regulatory changes. The authors introduce FinHardBench, a benchmark comprising 33 tasks, and conduct three types of experiments emulating real-world development workflows: module generation, system-level design space exploration (DSE) of a six-stage trading pipeline, and adaptation to specification changes. Across over 1,530 experiments, six LLMs achieved functional correctness rates of 19–61%, though some exhibited timing performance degradation up to 13.7×. Notably, the best-performing LLM consistently identified the globally optimal configuration in all five system-level DSE trials, significantly outperforming baseline methods such as random search, simulated annealing, and Bayesian optimization. These results reveal an inconsistency between LLMs’ code generation and architectural optimization capabilities, with task difficulty more closely tied to the availability of patterns in training data than to abstraction level.

Abstract

Can large language models generate not just correct, but fast hardware? This paper investigates the question in financial FPGA design, where 5-10 nanoseconds of latency determines competitive advantage and designs iterate continuously as protocols, strategies, and regulations evolve. FinHardBench, a benchmark of 33 financial computing tasks, is presented together with three experiments that mirror the real-world FPGA iteration cycle: generating new modules from specifications, tuning system-level configurations across a 6-stage trading pipeline, and adapting existing modules to specification changes. Evaluation of six LLMs on 1530+ experiment rounds yields three findings: (1) models achieve 19-61% functional correctness with timing degradation up to 13.7$\times$ on specific tasks; (2) in system-level design space exploration, top LLMs converge to the optimal configuration with higher reliability than random search, simulated annealing, and Bayesian optimization baselines (5/5 seeds vs. 0-4/5 at the same 24-round budget); (3) strategy-level specification changes remain unsolved for most models. Across the six models, generation and DSE rankings overlap moderately: the strongest code generator is not the fastest architecture optimizer, and the weakest code generator (MiniMax M2.7) still reaches the system optimum on 4 of 5 seeds. On the tasks in FinHardBench, difficulty tracks training data pattern availability more closely than abstraction level. FinHardBench is released as an open-source benchmark.

Problem

• LLM
• FPGA
• latency-aware hardware
• financial computing
• hardware generation

Innovation

• FinHardBench
• LLM-based hardware generation
• latency-aware FPGA design
• design space exploration
• financial computing