CreativEval: Evaluating Creativity of LLM-Based Hardware Code Generation

Understand

Large Language Models (LLMs) have proved effective and efficient in generating code, leading to their utilization within the hardware design process.

• Prior works evaluating LLMs' abilities for register transfer level code generation solely focus on functional correctness.
• However, the creativity associated with these LLMs, or the ability to generate novel and unique solutions, is a metric not as well understood, in part due to the challenge of quantifying this quality.
• To address this research gap, we present CreativeEval, a framework for evaluating the creativity of LLMs within the context of generating hardware designs.