dse-loop

Automate design space exploration by running programs and iteratively tuning parameters.

2|Updated Mar 29, 2026
One-click install
npx skills add https://github.com/satsuki-64/MiniAgentWorkflow --skill dse-loop-satsuki-64
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: dse-loop
Source: https://github.com/satsuki-64/MiniAgentWorkflow/tree/main/.skills/dse-loop
Command: npx skills add https://github.com/satsuki-64/MiniAgentWorkflow --skill dse-loop-satsuki-64

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

The DSE loop automates exploration of a design space by running a program, analyzing outputs, and iteratively tuning parameters until the objective is met or timeout, reducing manual trial-and-error in architecture and EDA workflows.

Core Features & Use Cases

  • Autonomous, iterative parameter tuning across design points
  • Configurable time budgets, iterations, and objective functions
  • Baseline generation, result logging, state recovery, and reproducibility for long-running experiments
  • Use cases include optimizing processor configurations, RTL parameters, and synthesis workflows.

Quick Start

Provide a baseline configuration and run the loop with default parameters to begin the exploration.

Frequently Asked Questions about dse-loop

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automate design space exploration for computer architecture parameters?

Automated design space exploration runs a program, analyzes outputs, and iteratively tunes architecture parameters until an objective is met or a timeout occurs, reducing manual trial-and-error workflows.

What is iterative parameter tuning in EDA and synthesis optimization?

Iterative parameter tuning in EDA autonomously explores synthesis configurations by running design points, logging results, and adjusting parameters across iterations to converge on optimum performance.

Can I set timeout limits and iteration caps for RTL parameterization workflows?

RTL parameterization workflows support configurable time budgets and iteration limits, allowing you to constrain long-running experiments and automatically halt when the exploration objective is met.

How do I recover state and results from a long-running formal verification experiment?

Long-running formal verification experiments maintain state recovery and result logging capabilities within a designated directory, ensuring reproducibility and allowing interrupted optimization workflows to resume.

Does this design space exploration approach require a baseline configuration to start?

Design space exploration requires a baseline configuration as a starting point, enabling the loop to generate initial results and systematically evaluate parameter variations against that baseline.

What are the limitations of automating parameter inferences for processor configurations?

Automating parameter inferences for processor configurations is bounded by configured time budgets and iteration limits, meaning complex design spaces may require multiple runs or manual adjustments to fully converge.