Skip to content

dse-loop

Category: drafting
Field: —
License: MIT
Updated: 2026-05-18
Stages: paper-drafting

DSE Loop: Autonomous Design Space Exploration

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:
  2. Program: what to run (command, script, or Makefile target)
  3. Parameter space: which knobs to tune and their ranges/options (may be incomplete — see step 2)
  4. Objective metric: what to optimize (and how to extract it from output)
  5. Constraints: hard limits that must not be violated (e.g., timing must close)
  6. Timeout: wall-clock budget

  7. Success criteria: when is the result "good enough" to stop early?

  8. 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:

Markdown
# 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).

  1. Read the project to understand:
  2. How to run the program
  3. Where results are produced (stdout, log files, reports)
  4. How to parse the objective metric from output

  5. Current/baseline configuration (if any)

  6. Create working directory: dse_results/ in project root

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

  12. dse_results/outputs/ — raw output for each run

  13. 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.

  14. 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:
    Text Only
    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

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:
  2. Look at the trend: which direction improves the metric?
  3. Avoid re-running configurations already evaluated

  4. Balance exploration (untested regions) vs exploitation (near current best)

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

  6. Run the program: execute and capture output

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

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

  9. Check stopping conditions:

  10. Timeout reached? → stop
  11. Max iterations reached? → stop
  12. Patience exhausted (no improvement in N iterations)? → stop
  13. Success criteria met (metric is "good enough")? → stop

  14. Constraint violation pattern detected? → adjust search bounds

  15. Update DSE_STATE.json:

    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
}

  16. 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:

Markdown
## 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 likely to exceed the timeout, stop and report.
  • Parse metrics programmatically — write a parsing script, don't eyeball logs
  • Keep raw outputs — save each run's full output in dse_results/outputs/iter_N/
  • Constraint violations are not improvements — a design point that violates constraints is never "best", regardless of the metric
  • If a run crashes, log the error, skip that point, and continue with the next
  • If the same crash repeats 3 times with different configs, stop and report the issue

Example Invocations

Text Only
## Minimal — just name the parameters, let the agent figure out ranges
/dse-loop "Run gem5 mcf benchmark. Tune: L1D_SIZE, L2_SIZE, ROB_ENTRIES. Objective: maximize IPC. Timeout: 3h"

## Partial — some ranges given, some not
/dse-loop "Run make synth. Tune: CLOCK_PERIOD [5ns, 4ns, 3ns, 2ns], FLATTEN, ABC_SCRIPT. Objective: minimize area at timing closure. Timeout: 1h"

## Fully specified — explicit ranges for everything
/dse-loop "Simulate processor with FIFO_DEPTH [4,8,16,32], ISSUE_WIDTH [1,2,4], PREFETCH [on,off]. Run: make sim. Objective: max throughput/area. Timeout: 2h"

## Real-world: PDAG-SFA formal verification tuning
/dse-loop "Run python run_bmc.py. Tune: BMC_DEPTH, ENGINE, TIMEOUT_PER_PROP. Objective: maximize properties proved. Timeout: 2h"