Toward Reliable LLM-Assisted Design Space Exploration under Performance, Cost, and Dependability Constraints

Architectural design space exploration (DSE) requires navigating large configuration spaces while satisfying multiple conflicting objectives, including performance, cost, and system dependability. Large language models (LLMs) have shown promise in assisting DSE by proposing candidate designs and interpreting simulation feedback. However, extending LLM-based DSE to realistic multi-objective settings introduces structural challenges. A naive multi-objective extension of prior LLM-based DSE approaches, which we term Co-Pilot2, exhibits reasoning instability, candidate degeneration, feasibility violations, and lack of progressive improvement. These limitations arise not from insufficient model capacity, but from the absence of structured control, verification, and decision integrity within the exploration process. 

To address these challenges, this research introduces REMODEL, a structured LLM-controlled DSE framework that transforms free-form reasoning into a constrained, verifiable, and iterative optimization process. REMODEL incorporates candidate pooling across parallel reasoning instances, strict state isolation via history snapshotting, deterministic feasibility verification, canonical design representation and deduplication, explicit decision stages, and structured reasoning to enforce complete parameter coverage and consistent trend analysis. These mechanisms enable reliable and stable exploration under complex multi-objective constraints. 

To support dependability-aware evaluation, the framework is integrated with cycle-accurate simulation using gem5 and its reliability-focused extension GemV, enabling detailed analysis of performance, power, and fault tolerance through vulnerability metrics. This integration allows the system to reason not only about performance–cost trade-offs, but also about reliability-aware design decisions under realistic execution conditions. 

Experimental evaluation demonstrates that REMODEL identifies near-optimal designs within a small number of simulations, achieving significantly higher solution quality per simulation compared to baseline methods such as random search and genetic algorithms, while maintaining low computational overhead. 

This work establishes a foundation for dependable LLM-assisted DSE by incorporating reliability constraints into the exploration loop. As a future direction, this framework will be extended to incorporate security-aware design considerations, enabling unified reasoning over performance, cost, reliability, and system security.