dse-loop

Design space exploration automates iterative parameter tuning by running target programs, parsing objective metrics, and selecting new parameters until goals are met or timeout occurs. It applies adaptive search strategies like structured sampling followed by directed refinement.

Tuning EDA optimization parameters requires an autonomous run-analyze-iterate loop that executes simulator-driven evaluations and extracts measurable results. Using an adaptive search strategy enables broad initial exploration followed by directed refinement across the design space.

Checkpoint recovery during architecture simulation relies on deterministic logging via state recovery files like DSE_STATE.json plus dse_log.csv. These files capture the exploration state, allowing the optimization loop to resume safely after interruptions without losing progress.

Autonomous parameter sweeps respect explicit timeout and iteration limits defined in the task request. Safety guardrails enforce these boundaries while running experiments, ensuring the optimization loop stops gracefully when constraints are reached without executing destructive commands.

Design space exploration supports programmatic objective parsing for simulator-driven performance evaluation tasks. You can specify objectives like maximizing IPC, and the system extracts these metrics from experiment logs to guide the adaptive search strategy.

EDA design parameter optimization enforces safety-first guardrails including no destructive commands and no killing unknown processes. These precautions prevent system damage during autonomous execution while maintaining deterministic logging for state recovery.