ChipGPT: How far are we from natural language hardware design
- URL: http://arxiv.org/abs/2305.14019v3
- Date: Mon, 19 Jun 2023 08:28:15 GMT
- Title: ChipGPT: How far are we from natural language hardware design
- Authors: Kaiyan Chang and Ying Wang and Haimeng Ren and Mengdi Wang and
Shengwen Liang and Yinhe Han and Huawei Li and Xiaowei Li
- Abstract summary: This work attempts to demonstrate an automated design environment that explores LLMs to generate hardware logic designs from natural language specifications.
We present a scalable four-stage zero-code logic design framework based on LLMs without retraining or finetuning.
- Score: 34.22592995908168
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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