Technical Building Blocks
Building reliable AI-native design loops requires a rigorous technical substrate. We cannot connect language models to hardware code generators and expect correct silicon. In Part II, we define four modular technical building blocks that every AI-native design loop needs: machine-usable hardware representations, learned design methods, tool-connected execution environments, and verification feedback.
We classify the structural representations that make hardware design state machine-usable, connect learned methods to cycle-accurate simulators and physical-design tools, and construct the verification feedback that detects and contains proxy gaming.
Part overview
- How do we maintain machine-usable state alignment between abstract system models and physical silicon constraints?
- What qualification rules prevent unverified candidate proposals from corrupting subsequent design search steps?
Chapter Roadmap
| Chapter | Core Architecture Question | Key Takeaway / Deliverable |
|---|---|---|
| 4 Architecture Data, Knowledge, and Representation | Which structural representations preserve hardware constraints for AI models? | Classifies structural representations (syntax trees, dataflow graphs, transaction-level models), data provenance, and access-bounded process knowledge. |
| 5 Prediction, Generation, and Optimization | How do prediction, generation, and optimization methods compare across architectural tasks? | Builds the role, method-family, and technique selection framework with the smallest-sufficient-approach rule. |
| 6 Execution Harnesses and Tool Isolation | How do we build scalable interfaces between AI loops and complex CAD toolchains? | Builds the tool, wrapper, harness, and environment split with pinned state, typed contracts, and execution records. |
| 7 Verification, Feedback, and Learning | How do we turn simulation and timing errors into verification feedback that drives search? | Establishes the qualify-then-claim-then-act sequence, proxy diagnostics, and independent checks. |