ResBench: Benchmarking LLM-Generated FPGA Designs with Resource Awareness

2025-03-11

AI Summary

Existing LLM-based HDL generation benchmarks evaluate only functional correctness, neglecting critical FPGA constraints—particularly hardware resource efficiency (e.g., LUT utilization)—and suffer from narrow scenario coverage, limiting their ability to distinguish models’ resource optimization capabilities. Method: We propose the first resource-efficiency–oriented benchmark for LLM-generated HDL: it encompasses 56 real-world FPGA designs across 12 application categories; introduces LUT, FF, and BRAM utilization as primary evaluation metrics; and establishes a scalable, resource-aware evaluation framework integrating Xilinx Vivado synthesis and implementation flows with automated comparative pipelines. Results: Experiments reveal substantial variation in LUT usage among state-of-the-art LLMs—up to 3.2×—demonstrating the benchmark’s strong discriminative power and practical utility for assessing and advancing resource-aware HDL generation.

Technology Category

• Machine Learning: Hardware-aware ML
• Natural Language Processing: Generation
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Field-Programmable Gate Arrays (FPGAs) are widely used in modern hardware design, yet writing Hardware Description Language (HDL) code for FPGA implementation remains labor-intensive and complex. Large Language Models (LLMs) have emerged as a promising tool for automating HDL generation, but existing benchmarks for LLM HDL code generation primarily evaluate functional correctness while overlooking the critical aspect of hardware resource efficiency. Moreover, current benchmarks lack diversity, failing to capture the broad range of real-world FPGA applications. To address these gaps, we introduce ResBench, the first resource-oriented benchmark explicitly designed to differentiate between resource-optimized and inefficient LLM-generated HDL. ResBench consists of 56 problems across 12 categories, covering applications from finite state machines to financial computing. Our evaluation framework systematically integrates FPGA resource constraints, with a primary focus on Lookup Table (LUT) usage, enabling a realistic assessment of hardware efficiency. Experimental results reveal substantial differences in resource utilization across LLMs, demonstrating ResBench's effectiveness in distinguishing models based on their ability to generate resource-optimized FPGA designs.

Problem

• Evaluates LLM-generated HDL code for FPGA resource efficiency.
• Addresses lack of diversity in current HDL benchmarks.
• Introduces ResBench to assess resource-optimized FPGA designs.

Innovation

• Introduces ResBench for resource-aware FPGA design benchmarking
• Focuses on LUT usage for hardware efficiency assessment
• Covers 56 problems across 12 diverse application categories