dse-loop

Automating design-space exploration involves running configurations, analyzing results, and autonomously selecting next parameters to optimize objectives. This Skill handles microarchitecture tuning and RTL exploration under user-specified budgets and constraints without manual intervention.

Yes, EDA parameter tuning supports configurable timeout, iteration limits, and safety constraints. You specify the budget and objective, and the loop autonomously explores and converges within those defined boundaries while exposing inferred ranges through a recoverable state.

Autonomous design-space exploration is the process of iteratively running configurations, analyzing results, and selecting next parameters without manual intervention. You need it for benchmark-driven microarchitecture tuning, synthesis parameterization, and RTL optimization.

You can recover an interrupted architecture optimization experiment using the DSE_STATE.json file. The experiment logging and recovery feature records every run, allowing state restoration to resume the autonomous exploration loop seamlessly.

Yes, design-space exploration applies across microarchitecture tuning, synthesis parameterization, and RTL exploration. It supports multiple benchmark-driven objectives and constraints, enabling iterative optimization across various architecture and EDA tasks.

Autonomous parameter tuning loops are bounded by your specified timeout, iteration limits, and safety constraints. Optimization quality depends on the defined objective and budget, and the exploration state must be managed via DSE_STATE.json for recoverability across architecture tasks.