---

name: dse-loop description: "Autonomous design space exploration loop for computer architecture and EDA. Runs a program, analyzes results, tunes parameters, and iterates until objective is met or timeout. Use when user says "DSE", "design space exploration", "sweep parameters", "optimize", "find best config", or wants iterative parameter tuning." argument-hint: "[task-description — include program, parameters, objective, and timeout]" allowed-tools: Bash(*), Read, Grep, Glob, Write, Edit

DSE Loop: Autonomous Design Space Exploration

🔁 Do not wrap this skill in /loop / CronCreate. It already loops internally until its objective is met or it times out. Unlike the verdict-bearing review/audit skills, its stop gate is an objective machine-checkable metric (Type-A), so its self-termination is safe same-model — the reason not to wrap it is scheduler duplication, not the verdict fence. See shared-references/external-cadence.md.

Autonomously explore a design space: run → analyze → pick next parameters → repeat, until the objective is met or timeout is reached. Designed for computer architecture and EDA problems.

Context: $ARGUMENTS

Safety Rules — READ FIRST

NEVER do any of the following:

• sudo anything
• rm -rf, rm -r, or any recursive deletion
• rm any file you did not create in this session
• Overwrite existing source files without reading them first
• git push, git reset --hard, or any destructive git operation
• Kill processes you did not start

If a step requires any of the above, STOP and report to the user.

Constants (override via $ARGUMENTS)

| Constant | Default | Description | |----------|---------|-------------| | TIMEOUT | 2h | Total wall-clock budget. Stop exploring after this. | | MAX_ITERATIONS | 50 | Hard cap on number of design points evaluated. | | PATIENCE | 10 | Stop early if no improvement for this many consecutive iterations. | | OBJECTIVE | minimize | minimize or maximize the target metric. |

Override inline: /dse-loop "task desc — timeout: 4h, max_iterations: 100, patience: 15"

Typical Use Cases

| Problem | Program | Parameters | Objective | |---------|---------|-----------|-----------| | Microarch DSE | gem5 simulation | cache size, assoc, pipeline width, ROB size, branch predictor | maximize IPC or minimize area×delay | | Synthesis tuning | yosys/DC script | optimization passes, target freq, effort level | minimize area at timing closure | | RTL parameterization | verilator sim | data width, FIFO depth, pipeline stages, buffer sizes | meet throughput target at min area | | Compiler flags | gcc/llvm build + benchmark | -O levels, unroll factor, vectorization, scheduling | minimize runtime or code size | | Placement/routing | openroad/innovus | utilization, aspect ratio, layer config | minimize wirelength / timing | | Formal verification | abc/sby | bound depth, engine, timeout per property | maximize coverage in time budget | | Memory subsystem | cacti / ramulator | bank count, row buffer policy, scheduling | optimize bandwidth/energy |

Workflow

Phase 0: Parse Task & Setup

1. Parse $ARGUMENTS to extract:

   • Program: what to run (command, script, or Makefile target)
   • Parameter space: which knobs to tune and their ranges/options (may be incomplete — see step 2)
   • Objective metric: what to optimize (and how to extract it from output)
   • Constraints: hard limits that must not be violated (e.g., timing must close)
   • Timeout: wall-clock budget
   • Success criteria: when is the result "good enough" to stop early?

2. Infer missing parameter ranges — If the user provides parameter names but NOT ranges/options, you MUST infer them before exploring:

   a. Read the source code — search for the parameter names in the codebase:

   • Look for argparse/click definitions, config files, Makefile variables, module parameters, #define, parameter (SystemVerilog), localparam, etc.
   • Extract defaults, types, and any comments hinting at valid values

   b. Apply domain knowledge to set reasonable ranges: | Parameter type | Inference strategy | |---------------|-------------------| | Cache/memory sizes | Powers of 2, typically 1KB–16MB | | Associativity | Powers of 2: 1, 2, 4, 8, 16 | | Pipeline width / issue width | Small integers: 1, 2, 4, 8 | | Buffer/queue/FIFO depth | Powers of 2: 4, 8, 16, 32, 64 | | Clock period / frequency | Based on technology node; try ±50% from default | | Bound depth (BMC/formal) | Geometric: 5, 10, 20, 50, 100 | | Timeout values | Geometric: 10s, 30s, 60s, 120s, 300s | | Boolean/enum flags | Enumerate all options found in source | | Continuous (learning rate, threshold) | Log-scale sweep: 5 points spanning 2 orders of magnitude around default | | Integer counts (threads, cores) | Linear: from 1 to hardware max |

   c. Start conservative — begin with 3-5 values per parameter. Expand range later if the best result is at a boundary.

   d. Log inferred ranges — write the inferred parameter space to dse_results/inferred_params.md so the user can review:

   # Inferred Parameter Space
   
   | Parameter | Source | Default | Inferred Range | Reasoning |
   |-----------|--------|---------|---------------|-----------|
   | CACHE_SIZE | config.py:42 | 32768 | [8192, 16384, 32768, 65536, 131072] | powers of 2, ±2x from default |
   | ASSOC | config.py:43 | 4 | [1, 2, 4, 8] | standard associativities |
   | BMC_DEPTH | run_bmc.py:15 | 10 | [5, 10, 20, 50] | geometric, common BMC depths |
   

   e. Boundary expansion — during the search, if the best result is at the min or max of a range, automatically extend that range by one step in that direction (but log the extension).

3. Read the project to understand:

   • How to run the program
   • Where results are produced (stdout, log files, reports)
   • How to parse the objective metric from output
   • Current/baseline configuration (if any)

4. Create working directory: dse_results/ in project root

   • dse_results/dse_log.csv — one row per design point
   • dse_results/DSE_REPORT.md — final report
   • dse_results/DSE_STATE.json — state for recovery
   • dse_results/inferred_params.md — inferred parameter space (if ranges were not provided)
   • dse_results/configs/ — config files for each run
   • dse_results/outputs/ — raw output for each run

5. Write a parameter extraction script (dse_results/parse_result.py or similar) that takes a run's output and returns the objective metric as a number. Test it on a baseline run first.

6. Run baseline (iteration 0): run the program with default/current parameters. Record the baseline metric. This is the point to beat.

Phase 1: Initial Exploration

Goal: Quickly survey the space to understand which parameters matter most.

Strategy: Latin Hypercube Sampling or structured sweep of key parameters.

1. Pick 5-10 diverse design points that span the parameter ranges
2. Run them (in parallel if independent, via background processes or sequential)
3. Record all results in dse_log.csv:

   iteration,param1,param2,...,metric,constraint_met,timestamp,notes
   0,default,default,...,baseline_val,yes,2026-03-13T10:00:00,baseline
   1,val1a,val2a,...,result1,yes,2026-03-13T10:05:00,initial sweep
   ...
   

4. Analyze: which parameters have the most impact on the objective?
5. Narrow the search to the most sensitive parameters

Phase 2: Directed Search

Goal: Converge toward the optimum by making informed choices.

Strategy: Adaptive — pick the approach that fits the problem:

• Few parameters (≤3): Fine-grained grid search around the best region from Phase 1
• Many parameters (>3): Coordinate descent — optimize one parameter at a time, holding others at current best
• Binary/categorical params: Enumerate promising combinations
• Continuous params: Binary search or golden section between best neighbors
• Multi-objective: Track Pareto frontier, explore along the front

For each iteration:

1. Select next design point based on results so far:

   • Look at the trend: which direction improves the metric?
   • Avoid re-running configurations already evaluated
   • Balance exploration (untested regions) vs exploitation (near current best)

2. Modify parameters: edit config file, command-line args, or source constants

3. Run the program: execute and capture output

4. Parse results: extract the objective metric and check constraints

5. Log to dse_log.csv: append the new row

6. Check stopping conditions:

   • Timeout reached? → stop
   • Max iterations reached? → stop
   • Patience exhausted (no improvement in N iterations)? → stop
   • Success criteria met (metric is "good enough")? → stop
   • Constraint violation pattern detected? → adjust search bounds

7. Update DSE_STATE.json:

   {
     "iteration": 15,
     "status": "in_progress",
     "best_metric": 1.23,
     "best_params": {"cache_size": 32768, "assoc": 4, "pipeline_width": 2},
     "total_iterations": 15,
     "start_time": "2026-03-13T10:00:00",
     "timeout": "2h",
     "patience_counter": 3
   }
   

8. Decide next step → back to step 1

Phase 3: Refinement (if time allows)

If the search converged and there's still time budget:

1. Local perturbation: try ±1 step on each parameter from the best point
2. Sensitivity analysis: which parameters can be relaxed without hurting the metric?
3. Constraint boundary: if a constraint is nearly binding, explore near-feasible points

Phase 4: Report

Write dse_results/DSE_REPORT.md:

# Design Space Exploration Report

**Task**: [description]
**Date**: [start] → [end]
**Total iterations**: N
**Wall-clock time**: X hours Y minutes

## Objective
- **Metric**: [what was optimized]
- **Direction**: minimize / maximize
- **Baseline**: [value]
- **Best found**: [value] ([improvement]% better than baseline)

## Best Configuration
| Parameter | Baseline | Best |
|-----------|----------|------|
| param1    | default  | best_val |
| param2    | default  | best_val |
| ...       | ...      | ... |

## Search Trajectory
| Iteration | param1 | param2 | ... | Metric | Notes |
|-----------|--------|--------|-----|--------|-------|
| 0 (baseline) | ... | ... | ... | ... | baseline |
| 1 | ... | ... | ... | ... | initial sweep |
| ... | ... | ... | ... | ... | ... |
| N (best) | ... | ... | ... | ... | ★ best |

## Parameter Sensitivity
- **param1**: [high/medium/low impact] — [brief explanation]
- **param2**: [high/medium/low impact] — [brief explanation]

## Pareto Frontier (if multi-objective)
[Table or description of non-dominated points]

## Stopping Reason
[timeout / max_iterations / patience / success_criteria_met]

## Recommendations
- [actionable insights from the exploration]
- [which parameters matter most]
- [suggested follow-up explorations]

Also generate a summary plot if matplotlib is available:

• Convergence curve (metric vs iteration)
• Parameter sensitivity bar chart
• Pareto frontier scatter (if multi-objective)

State Recovery

If the context window compacts mid-run, the loop recovers from DSE_STATE.json + dse_log.csv:

1. Read DSE_STATE.json for current iteration, best params, patience counter
2. Read dse_log.csv for full history
3. Resume from next iteration

Key Rules

• Work AUTONOMOUSLY — do not ask the user for permission at each iteration
• Every run must be logged — even failed runs, constraint violations, errors. The log is the ground truth.
• Never re-run an identical configuration — check dse_log.csv before each run
• Respect the timeout — check elapsed time before starting a new iteration. If the next run is likel