AI-Accelerated Agile Hardware Design Using the ROHD Framework (intel.github.io)

A new blog demonstrates how AI can accelerate correct, agile VLSI design by pairing LLMs with the ROHD hardware-construction framework (Dart-based) and an always-on edit/simulate loop. Rather than “push-button” HDL generation, the team uses a test-driven, feature-by-feature methodology where the designer stays in the loop and LLMs (they used Claude Sonnet 4) synthesize modular components, tests, and simulation harnesses. Key enablers are ROHD’s high-level abstractions (ReadyValid interfaces, LogicStructure types like RequestStructure/ResponseStructure), Dart familiarity in LLMs, and AI coding agents (Dart MCP servers + VS Code Copilot) that bridge agents to the IDE and speed iteration. The result: rapid, few-iteration convergence on working hardware with focused correctness checks instead of sprawling buggy outputs. Technically, the case study builds a RequestResponseChannel (base class API shown) and evolves it into a caching request/response channel that uses a fully-associative CAM (id-as-tag, addr-as-data), a response ReadyValidFifo (id+data), and ready/valid flow-control rules. The LLM produced working forwarding and buffered versions, then implemented cache-miss/match behavior: misses are forwarded and stored in the CAM; downstream responses are tag-matched to update cache and enqueue upstream responses. The team used incremental prompts and targeted cycle-accurate tests (e.g., 1-miss→1-hit and multi-miss then hit sequences) to expose semantic edge cases (blocking when FIFOs are full, sampling timing) and quickly fix them. This demonstrates a practical workflow for integrating LLMs into hardware toolchains: modular APIs, tight simulation feedback, and directed testing make AI-assisted hardware both faster and safer.