mlsanityData to 7 Oct 2026

Designing Silicon Brains using LLM: Leveraging ChatGPT for Automated Description of a Spiking Neuron Array

arXiv 2402.10920 ↗First posted 25 Jan 2024cs.AR cs.AI cs.ETcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 5 of its 11 references that are in the map. First posted on arXiv 25 Jan 2024.

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5 of 11 references.

References · 5

  1. 2023GPT-4 Technical Reportcited by 18,132
  2. 2023Chip-Chat: Challenges and Opportunities in Conversational Hardware Designcited by 136
  3. 2023ChipGPT: How far are we from natural language hardware designcited by 101
  4. 2023Is ChatGPT the Ultimate Programming Assistant -- How far is it?cited by 72
  5. 2023ChatGPT for Programming Numerical Methodscited by 26

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