Designing Silicon Brains using LLM: Leveraging ChatGPT for Automated Description of a Spiking Neuron Array
This work addresses the challenge of natural language-driven hardware design for engineers, but it is incremental as it applies an existing LLM method to a new domain without major methodological innovations.
The paper tackled the problem of automating hardware design by using ChatGPT4 to generate a synthesizable Verilog description for a programmable Spiking Neuron Array ASIC, resulting in a design that was verified in simulation and submitted for fabrication in Skywater 130nm through Tiny Tapeout 5.
Large language models (LLMs) have made headlines for synthesizing correct-sounding responses to a variety of prompts, including code generation. In this paper, we present the prompts used to guide ChatGPT4 to produce a synthesizable and functional verilog description for the entirety of a programmable Spiking Neuron Array ASIC. This design flow showcases the current state of using ChatGPT4 for natural language driven hardware design. The AI-generated design was verified in simulation using handcrafted testbenches and has been submitted for fabrication in Skywater 130nm through Tiny Tapeout 5 using an open-source EDA flow.