How OpenAI Used Its Own LLMs to Design Its Jalapeño Chip

说明与用途

OpenAI 的 LLM 加速了 Jalapeño 芯片的 RTL 生成与验证，显著缩短了开发周期。

问题

Designing a custom chip (Jalapeño) required rapid, accurate RTL generation and verification, traditionally a time‑consuming, error‑prone process.

结果

The LLM‑driven workflow reduced RTL development time by ~70%, lowered bug count, and accelerated the Jalapeño chip’s time‑to‑market.

工作流

• 1. Define high‑level functional spec in natural language.
• 2. Prompt OpenAI’s LLM to generate RTL code.
• 3. Run automated linting and design‑rule checks; LLM refines code.
• 4. Generate testbenches and run simulation; LLM adjusts RTL as needed.
• 5. Iterate until functional coverage and performance targets met.

证据

OpenAI’s LLMs generated RTL from high‑level specifications and auto‑fixed design‑rule violations.

来源