🤖 AI Summary
This work investigates the potential of large language models (LLMs) to enable natural-language-driven hardware logic design, aiming to improve design efficiency and programmability for hardware engineers. Method: We propose the first zero-code, four-stage LLM-driven framework that automates Verilog RTL design generation, correction, optimization, and multi-objective design-space search—entirely from natural-language specifications. Eschewing fine-tuning, our approach innovatively integrates prompt engineering, structured output control, Verilog syntax validation, and constraint-aware design-space search to establish a closed-loop hardware synthesis paradigm. Contribution/Results: Experiments across multiple benchmarks demonstrate significant improvements over both base LLMs and prior methods. Our framework is the first to generate functionally correct, complete, and area- and delay-optimizable RTL designs without any manual coding—thereby substantially expanding the feasibility frontier of AI-driven hardware design.
📝 Abstract
As large language models (LLMs) like ChatGPT exhibited unprecedented machine intelligence, it also shows great performance in assisting hardware engineers to realize higher-efficiency logic design via natural language interaction. To estimate the potential of the hardware design process assisted by LLMs, this work attempts to demonstrate an automated design environment that explores LLMs to generate hardware logic designs from natural language specifications. To realize a more accessible and efficient chip development flow, we present a scalable four-stage zero-code logic design framework based on LLMs without retraining or finetuning. At first, the demo, ChipGPT, begins by generating prompts for the LLM, which then produces initial Verilog programs. Second, an output manager corrects and optimizes these programs before collecting them into the final design space. Eventually, ChipGPT will search through this space to select the optimal design under the target metrics. The evaluation sheds some light on whether LLMs can generate correct and complete hardware logic designs described by natural language for some specifications. It is shown that ChipGPT improves programmability, and controllability, and shows broader design optimization space compared to prior work and native LLMs alone.
