Closed-Loop Execution and Ownership
When we transition from technical foundations to full-system operation, our central challenge shifts from point AI assistance bolted onto isolated tasks to operating complete, closed-loop design workflows and evaluating them rigorously. In Part III, we demonstrate how to run end-to-end design loops across spatial accelerators and memory subsystems, measure what transfers across design problems, and evaluate automated systems against matched baselines without falling prey to confirmation bias.
We examine how closed-loop exploration operates across multi-fidelity tools, how requalification contracts bound negative transfer, and how total-cost accounting and red-teaming defenses establish credible empirical evidence for AI-native architecture workflows. The part then closes the book’s central argument. We separate what our evidence supports from what a named architect backed by an accountable organization decides to commit, and we return to the Lighthouse prompt that opened the book to state plainly what our own results establish and what they leave open.
Part overview
- How do we execute stage-gated evaluation loops that reject unsound proposals before incurring expensive physical design costs?
- What governance frameworks ensure that automated tool decisions preserve human architectural ownership and commitment authority?
Chapter Roadmap
| Chapter | Core Architecture Question | Key Takeaway / Deliverable |
|---|---|---|
| 8 Closed-Loop Design Execution | How does an end-to-end design loop execute across spatial accelerators and memory models? | Demonstrates an executed study evaluating systolic array candidates against SCALE-Sim and Ramulator. |
| 9 Pattern Transferability and Generalization | What design mechanisms and offload bounds generalize across distinct hardware problems? | Formulates LogCA offload bounds, requalification contracts, and Triton-to-RVV 1.0 lowering paths. |
| 10 System Evaluation and Red-Teaming | How do we evaluate AI workflow quality and defend against LLM judge confirmation bias? | Establishes the four evaluation objects, matched complete-workflow comparisons, total-cost accounting, and red-teaming. |
| 11 Architectural Ownership and Commitment | What technical work do we still own when the system can do the rest, and who commits the design? | Separates the supported technical recommendation from the commitment decision, then returns to the Lighthouse to state what our results do and do not justify. |