Bench4HLS: End-to-End Evaluation of LLMs in High-Level Synthesis Code Generation

In recent developments within the field of technology, especially pertaining to the synthesis of digital circuitry, researchers have explored the application of large language models (LLMs) in high-level synthesis (HLS) code generation. Traditionally, LLMs have demonstrated robust capabilities in generating code for hardware design, typically operating at the register-transfer level (RTL). This involves the detailed design of digital logic at a low abstraction level, which had been the predominant focus up until now. However, a new study titled 'Bench4HLS: End-to-End Evaluation of LLMs in High-Level Synthesis Code Generation' has surfaced, indicating a shift in focus from RTL to HLS. High-level synthesis represents a higher abstraction level in digital design, focusing on converting high-level design entries, like algorithms, into hardware descriptions that can be synthesized onto digital circuits. This transition from RTL to HLS in LLM applications underscores a growing interest in facilitating more efficient hardware design processes that leverage high-level inputs, potentially expediting design workflows in industries reliant on fast iterative design and testing. This study highlights a notable increase in the academic exploration of HLS-related code generation using LLMs compared to RTL. In just six months, the ratio of HLS-focused studies relative to RTL has doubled, moving from 1:10 to 2:10. This shift not only reflects advancements in LLM technologies but also the increasing confidence of researchers and engineers in utilizing these models for more complex, high-level tasks. This trend is suggestive of a potential paradigm shift where the role of human designers might evolve from crafting detailed low-level designs to supervising LLMs that execute high-level design syntax effectively, thereby enhancing the efficiency and scalability of digital hardware development. The paper 'Bench4HLS' appears on the arXiv platform with the identifier 2601.19941v1 and belongs to a cross-disciplinary category, merging insights from fields such as computer science, artificial intelligence, and electrical engineering. As these models become more adept at handling high-level design complications, their utility in streamlining synthesis and reducing human error could prove transformative, paving the way for pioneering innovations in hardware design and implementation.