Analysis of LLM-models in optimizing and designing VHDL code

Abstract

In the evolving world of software and hardware co-design, VHDL has emerged as a pivotal lan-guage for hardware description. The objective of this research was to harness the capabilities of Large Language Models (LLM) to optimize and streamline VHDL code design. The underlying premise was rooted in the belief that feeding vast amounts of raw VHDL data to these models would enable them to autonomously learn and generate efficient VHDL code snippets.

This study adopted a systematic approach involving the fine-tuning of pre-existing LLMs, specifi-cally focusing on model adaptations for VHDL code generation. The methodology encompassed iterative processes, characterized by several adjustments to parameters, data parallelism lever-aging, and base model load configurations. Additionally, considerations like unsupervised versus supervised learning approaches were evaluated, underscoring the pivotal role of labeled data for targeted outcomes.

The empirical findings shed light on the significance of data quality over quantity. While the ini-tial approach embraced a 'feed and learn' attitude, it became evident that for fine-tuning purpos-es, curated and labeled data was paramount. Testing of the refined models, against a set of basic to advanced VHDL challenges, showcased the potential of the LLMs, with ChatGPT mod-els outperforming others considerably.

In conclusion, while the journey unveiled the intricacies of training and fine-tuning LLMs for VHDL, it also highlighted the invaluable role of data. Future endeavors in this domain can delve deeper into supervised training methods, base model variations, and the intricate interplay of quantization during training. The research accentuates the promise of LLMs in the domain of VHDL design, steering a path for more refined and nuanced implementations in the future.