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dse-loop

Automate iterative parameter tuning for electronic design automation and computer architecture workflows.

2|Updated Aug 12, 2025
Search Tags:#eda#microarchitecture#parameter tuning#dse#design space exploration#computer architecture#synthesis optimization
One-click install
npx skills add https://github.com/goupup-ai/miccai25 --skill dse-loop-goupup-ai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dse-loop
Source: https://github.com/goupup-ai/miccai25/tree/main/ARIS/skills/dse-loop
Command: npx skills add https://github.com/goupup-ai/miccai25 --skill dse-loop-goupup-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Manually tuning parameters for EDA tools, computer architecture simulations, and related engineering workflows is extremely time-consuming, requires deep domain expertise, and often fails to find optimal configurations within reasonable time budgets. This Skill automates the entire design space exploration process to eliminate manual effort and accelerate optimization.

Core Features & Use Cases

  • Autonomous Iterative Loop: Runs target programs, analyzes output metrics, adjusts parameters, and repeats until the optimization objective is met, timeout is reached, or no further improvement is detected.
  • Broad Use Case Support: Handles microarchitecture tuning (e.g., gem5 simulations), logic synthesis optimization, RTL parameter adjustment, compiler flag tuning, place-and-route optimization, and formal verification configuration.
  • Built-in Guardrails: Includes safety rules to prevent destructive operations, state recovery for context window compaction, constraint checking, and early stopping to avoid wasted compute.
  • Example Use Case: A chip design engineer can use this Skill to automatically find the optimal cache size, associativity, and pipeline width for a new processor core to maximize IPC without manually running and analyzing hundreds of simulations.

Quick Start

Use the dse-loop skill to optimize the parameters of your EDA tool or architecture simulator by providing the program to run, the parameters to tune, the objective metric to maximize or minimize, and your total time budget.

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 logic synthesis and RTL optimization?▼

Automate parameter tuning for logic synthesis by running an autonomous iterative loop that adjusts RTL parameters, analyzes output metrics, and repeats until your optimization objective is met. This eliminates manual configuration effort and accelerates finding optimal setups.

What is design space exploration in computer architecture and microarchitecture?▼

Design space exploration in microarchitecture is the process of iteratively tuning parameters like cache size or pipeline width in simulators to maximize performance metrics. Automating this process eliminates manual runs and accelerates optimal core configuration discovery.

Can I use autonomous parameter tuning for place-and-route and compiler flag optimization?▼

Yes, you can use autonomous parameter tuning for place-and-route optimization and compiler flag tuning. The iterative loop runs your target programs, evaluates machine-checkable objectives, and adjusts configurations automatically within your defined time budget.

How do I prevent wasted compute during microarchitecture design space exploration?▼

Prevent wasted compute during microarchitecture design space exploration by using built-in safety guardrails and early stopping. The autonomous loop automatically halts iterations when no further improvement is detected or the timeout is reached, avoiding unnecessary simulations.

Does automated EDA parameter tuning support state recovery for long-running synthesis optimization?▼

Yes, automated EDA parameter tuning supports state recovery for long-running synthesis optimization. It includes safety rules to prevent destructive operations and manages context window compaction, ensuring iterative tuning processes resume correctly without losing progress.

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