dse-loop

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

Updated Apr 10, 2026
One-click install
npx skills add https://github.com/KwongFuk/codex-skills --skill dse-loop-kwongfuk
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dse-loop
Source: https://github.com/KwongFuk/codex-skills/tree/main/global/dse-loop
Command: npx skills add https://github.com/KwongFuk/codex-skills --skill dse-loop-kwongfuk

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Autonomous design space exploration reduces manual trial-and-error by running a target program, collecting results, and iteratively tuning parameters until a defined objective is achieved within a timeout.

Core Features & Use Cases

  • Autonomous run-analyze-tune loop for design spaces in architecture/EDA contexts.
  • Handles timeouts, iteration caps, and patience-based stopping; logs results and supports recovery from DSE_STATE.json.
  • Useful for exploring program configurations, hardware knobs, compiler flags, and parameterized simulations to maximize a metric like IPC or minimize area/latency.

Quick Start

Invoke /dse-loop with your program and tuneable parameters, specify the objective and timeout, and start autonomous 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 parameter tuning for architecture and EDA optimization tasks?▼

Automate parameter tuning by running a target program, collecting results, and iteratively adjusting configurations until a defined objective is achieved within a specified timeout. The autonomous run-analyze-tune loop handles iteration caps and logs results for architecture and EDA optimization.

What is autonomous design space exploration and how does it work?▼

Autonomous design space exploration reduces manual trial-and-error by executing a program, parsing outcomes into a numeric objective, and iteratively tuning parameters. It handles timeouts, patience-based stopping, and logs progress through DSE_STATE.json and dse_log.csv for recovery.

How do I explore compiler flags and hardware knobs to maximize metrics like IPC?▼

Explore compiler flags and hardware knobs by invoking the exploration loop with your parameterized program. Specify a numeric objective, such as maximizing IPC or minimizing area and latency, and let the autonomous process sweep configurations until the timeout or iteration limit is reached.

Can I recover an interrupted design space exploration from previous logs?▼

You can recover interrupted design space exploration using the DSE_STATE.json file. The loop supports state recovery alongside logging outcomes to dse_log.csv, allowing the autonomous tuning process to resume from its last saved checkpoint.

What do I need to set up before running autonomous parameter sweeps for hardware-software co-design?▼

You need a runtime environment to execute programs, a mechanism to parse program outcomes into a numeric objective, and support for timeouts and iteration caps. These prerequisites enable the autonomous loop to sweep configurations for hardware-software co-design experiments.

What are the limitations of using an autonomous loop for system configuration sweeps?▼

Limitations include dependency on a defined numeric objective to evaluate outcomes and strict timeout or iteration caps that halt the process. The autonomous loop requires a runtime to execute programs and cannot tune parameters without a parseable success metric.