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.