Debug playbook

Debug Playbook for Hyperparameters & Schedulers focuses on best validation score per compute hour, convergence speed, and run-to-run variance. The goal is to connect model behavior to hardware-aware decisions and operational risk.

AI-VLSI debug is layer-first. Find the first failing layer and avoid random tuning before proving mechanism.

Root-cause tree

ROOT-CAUSE TREE — Hyperparameters & Schedulers

best validation score per compute hour, convergence speed, and run-to-run variance regressed
        |
   data/model drift?
     /             \
   yes              no
   |                 |
dataset / labels   runtime / hardware / mapping
check leakage      check bandwidth, precision,
and split policy   scheduling, implementation

Always isolate first failing layer before fixes.

1. Freeze dataset, model, runtime, and hardware revision tags.

2. Isolate first failing workload slice and metric shift.

3. Classify issue: data drift, model behavior, runtime mismatch, or hardware bottleneck.

4. Prove hypothesis with one reduced reproducible experiment.

5. Patch smallest owner-controlled fix.

6. Re-run full quality + performance + stability validation.

Review memo template

AI-VLSI REVIEW MEMO — Training Data Pipeline / Hyperparameters & Schedulers

1. Symptom
   - Watched metric: best validation score per compute hour, convergence speed, and run-to-run variance
   - Failing workload slice: <name>
   - Failing layer: <data/model/runtime/hardware>
   - Revision tags: <dataset, model, runtime, hardware>

2. Mechanism hypothesis
   - Primary mechanism: Learning rate, batch size, and scheduler policy dominate training efficiency and final model quality under finite compute budget.
   - Competing hypotheses: <data drift, optimization bug, mapping bottleneck>
   - Missing evidence: <profile, calibration, signoff correlation>

3. Proposed action
   - Minimal reversible change: <config/model/runtime/hardware tweak>
   - Expected movement: <quality/latency/power>
   - Regression risk: distribution shift, stack incompatibility, owner handoff

4. Signoff
   - Re-run artifact: experiment tracker snapshot, scheduler comparison, and tuning notebook
   - Required owners: ML engineer, MLOps owner, compute budget owner
   - Final decision: ship, rollback, bounded pilot, or escalate

AI-VLSI deep dive

Data and experiment discipline are the top predictors of production reliability.

Concept diagram

DATA PIPELINE

ingest -> clean -> split -> train -> tune -> scale

Metric graph

PIPELINE RISK

data leakage           ███████
overfit drift          █████
reproducibility gaps   ████

Reports and artifacts

• split audit

• augmentation effect report

• hyperparameter tracker

• scaling efficiency chart

Mini case study

A minor split leakage inflated validation gains and delayed a critical workflow decision.

Debug branches

• Rebuild split lineage

• Check seed reproducibility

• Re-run baseline before tuning

Senior review question

Ask: what evidence connects this ML claim to a concrete VLSI workflow decision and owner signoff?

Key takeaways

• Every AI claim should map to a measurable engineering outcome.

• Validate both model quality and hardware/runtime feasibility before adoption.

Common pitfalls

• Optimizing benchmark metrics that do not correlate with signoff goals.

• Ignoring data drift and calibration after deployment.

• Shipping ML workflows without clear rollback ownership.

Execution drill pack 1

Use this pack to rehearse AI-for-VLSI decision making on ai-vlsi/training-data-pipeline/hyperparameters-and-schedulers/debug-playbook: metric framing, mechanism proof, hardware implications, and release safety.

Evidence checklist

• Metric context includes workload, dataset slice, and revision tags.

• Mechanism explanation links model behavior to observed outcome.

• Hardware/runtime feasibility is profiled, not assumed.

• Owner and rollback path are documented before rollout.

Review prompts

1. Which decision will this model output influence?

2. What is the first failing layer when metric regresses?

3. Which owner applies the smallest reversible fix?

4. What validation matrix is required before deployment?

Evidence capsule

AI-VLSI EVIDENCE CAPSULE 1

PATH: ai-vlsi/training-data-pipeline/hyperparameters-and-schedulers/debug-playbook
WORKLOAD SLICE: <name>
PRIMARY METRIC: <value/trend>
FIRST FAILING LAYER: <data/model/runtime/hardware>
OWNER: <name>
PRIMARY ARTIFACT: <report/profile/dashboard>
DECISION: <ship / rollback / escalate>

Execution drill pack 2

Use this pack to rehearse AI-for-VLSI decision making on ai-vlsi/training-data-pipeline/hyperparameters-and-schedulers/debug-playbook: metric framing, mechanism proof, hardware implications, and release safety.

Evidence checklist

• Metric context includes workload, dataset slice, and revision tags.

• Mechanism explanation links model behavior to observed outcome.

• Hardware/runtime feasibility is profiled, not assumed.

• Owner and rollback path are documented before rollout.

Review prompts

1. Which decision will this model output influence?

2. What is the first failing layer when metric regresses?

3. Which owner applies the smallest reversible fix?

4. What validation matrix is required before deployment?

Evidence capsule

AI-VLSI EVIDENCE CAPSULE 2

PATH: ai-vlsi/training-data-pipeline/hyperparameters-and-schedulers/debug-playbook
WORKLOAD SLICE: <name>
PRIMARY METRIC: <value/trend>
FIRST FAILING LAYER: <data/model/runtime/hardware>
OWNER: <name>
PRIMARY ARTIFACT: <report/profile/dashboard>
DECISION: <ship / rollback / escalate>

Execution drill pack 3

Use this pack to rehearse AI-for-VLSI decision making on ai-vlsi/training-data-pipeline/hyperparameters-and-schedulers/debug-playbook: metric framing, mechanism proof, hardware implications, and release safety.

Evidence checklist

• Metric context includes workload, dataset slice, and revision tags.

• Mechanism explanation links model behavior to observed outcome.

• Hardware/runtime feasibility is profiled, not assumed.

• Owner and rollback path are documented before rollout.

Review prompts

1. Which decision will this model output influence?

2. What is the first failing layer when metric regresses?

3. Which owner applies the smallest reversible fix?

4. What validation matrix is required before deployment?

Evidence capsule

AI-VLSI EVIDENCE CAPSULE 3

PATH: ai-vlsi/training-data-pipeline/hyperparameters-and-schedulers/debug-playbook
WORKLOAD SLICE: <name>
PRIMARY METRIC: <value/trend>
FIRST FAILING LAYER: <data/model/runtime/hardware>
OWNER: <name>
PRIMARY ARTIFACT: <report/profile/dashboard>
DECISION: <ship / rollback / escalate>

Execution drill pack 4

Use this pack to rehearse AI-for-VLSI decision making on ai-vlsi/training-data-pipeline/hyperparameters-and-schedulers/debug-playbook: metric framing, mechanism proof, hardware implications, and release safety.

Evidence checklist

• Metric context includes workload, dataset slice, and revision tags.

• Mechanism explanation links model behavior to observed outcome.

• Hardware/runtime feasibility is profiled, not assumed.

• Owner and rollback path are documented before rollout.

Review prompts

1. Which decision will this model output influence?

2. What is the first failing layer when metric regresses?

3. Which owner applies the smallest reversible fix?

4. What validation matrix is required before deployment?

Evidence capsule

AI-VLSI EVIDENCE CAPSULE 4

PATH: ai-vlsi/training-data-pipeline/hyperparameters-and-schedulers/debug-playbook
WORKLOAD SLICE: <name>
PRIMARY METRIC: <value/trend>
FIRST FAILING LAYER: <data/model/runtime/hardware>
OWNER: <name>
PRIMARY ARTIFACT: <report/profile/dashboard>
DECISION: <ship / rollback / escalate>

Execution drill pack 5

Use this pack to rehearse AI-for-VLSI decision making on ai-vlsi/training-data-pipeline/hyperparameters-and-schedulers/debug-playbook: metric framing, mechanism proof, hardware implications, and release safety.

Evidence checklist

• Metric context includes workload, dataset slice, and revision tags.

• Mechanism explanation links model behavior to observed outcome.

• Hardware/runtime feasibility is profiled, not assumed.

• Owner and rollback path are documented before rollout.

Review prompts

1. Which decision will this model output influence?

2. What is the first failing layer when metric regresses?

3. Which owner applies the smallest reversible fix?

4. What validation matrix is required before deployment?

Evidence capsule

AI-VLSI EVIDENCE CAPSULE 5

PATH: ai-vlsi/training-data-pipeline/hyperparameters-and-schedulers/debug-playbook
WORKLOAD SLICE: <name>
PRIMARY METRIC: <value/trend>
FIRST FAILING LAYER: <data/model/runtime/hardware>
OWNER: <name>
PRIMARY ARTIFACT: <report/profile/dashboard>
DECISION: <ship / rollback / escalate>

Execution drill pack 6

Use this pack to rehearse AI-for-VLSI decision making on ai-vlsi/training-data-pipeline/hyperparameters-and-schedulers/debug-playbook: metric framing, mechanism proof, hardware implications, and release safety.

Evidence checklist

• Metric context includes workload, dataset slice, and revision tags.

• Mechanism explanation links model behavior to observed outcome.

• Hardware/runtime feasibility is profiled, not assumed.

• Owner and rollback path are documented before rollout.

Review prompts

1. Which decision will this model output influence?

2. What is the first failing layer when metric regresses?

3. Which owner applies the smallest reversible fix?

4. What validation matrix is required before deployment?

Evidence capsule

AI-VLSI EVIDENCE CAPSULE 6

PATH: ai-vlsi/training-data-pipeline/hyperparameters-and-schedulers/debug-playbook
WORKLOAD SLICE: <name>
PRIMARY METRIC: <value/trend>
FIRST FAILING LAYER: <data/model/runtime/hardware>
OWNER: <name>
PRIMARY ARTIFACT: <report/profile/dashboard>
DECISION: <ship / rollback / escalate>

Execution drill pack 7

Use this pack to rehearse AI-for-VLSI decision making on ai-vlsi/training-data-pipeline/hyperparameters-and-schedulers/debug-playbook: metric framing, mechanism proof, hardware implications, and release safety.

Evidence checklist

• Metric context includes workload, dataset slice, and revision tags.

• Mechanism explanation links model behavior to observed outcome.

• Hardware/runtime feasibility is profiled, not assumed.

• Owner and rollback path are documented before rollout.

Review prompts

1. Which decision will this model output influence?

2. What is the first failing layer when metric regresses?

3. Which owner applies the smallest reversible fix?

4. What validation matrix is required before deployment?

Evidence capsule

AI-VLSI EVIDENCE CAPSULE 7

PATH: ai-vlsi/training-data-pipeline/hyperparameters-and-schedulers/debug-playbook
WORKLOAD SLICE: <name>
PRIMARY METRIC: <value/trend>
FIRST FAILING LAYER: <data/model/runtime/hardware>
OWNER: <name>
PRIMARY ARTIFACT: <report/profile/dashboard>
DECISION: <ship / rollback / escalate>

Execution drill pack 8

Use this pack to rehearse AI-for-VLSI decision making on ai-vlsi/training-data-pipeline/hyperparameters-and-schedulers/debug-playbook: metric framing, mechanism proof, hardware implications, and release safety.

Evidence checklist

• Metric context includes workload, dataset slice, and revision tags.

• Mechanism explanation links model behavior to observed outcome.

• Hardware/runtime feasibility is profiled, not assumed.

• Owner and rollback path are documented before rollout.

Review prompts

1. Which decision will this model output influence?

2. What is the first failing layer when metric regresses?

3. Which owner applies the smallest reversible fix?

4. What validation matrix is required before deployment?

Evidence capsule

AI-VLSI EVIDENCE CAPSULE 8

PATH: ai-vlsi/training-data-pipeline/hyperparameters-and-schedulers/debug-playbook
WORKLOAD SLICE: <name>
PRIMARY METRIC: <value/trend>
FIRST FAILING LAYER: <data/model/runtime/hardware>
OWNER: <name>
PRIMARY ARTIFACT: <report/profile/dashboard>
DECISION: <ship / rollback / escalate>

Execution drill pack 9

Use this pack to rehearse AI-for-VLSI decision making on ai-vlsi/training-data-pipeline/hyperparameters-and-schedulers/debug-playbook: metric framing, mechanism proof, hardware implications, and release safety.

Evidence checklist

• Metric context includes workload, dataset slice, and revision tags.

• Mechanism explanation links model behavior to observed outcome.

• Hardware/runtime feasibility is profiled, not assumed.

• Owner and rollback path are documented before rollout.

Review prompts

1. Which decision will this model output influence?

2. What is the first failing layer when metric regresses?

3. Which owner applies the smallest reversible fix?

4. What validation matrix is required before deployment?

Evidence capsule

AI-VLSI EVIDENCE CAPSULE 9

PATH: ai-vlsi/training-data-pipeline/hyperparameters-and-schedulers/debug-playbook
WORKLOAD SLICE: <name>
PRIMARY METRIC: <value/trend>
FIRST FAILING LAYER: <data/model/runtime/hardware>
OWNER: <name>
PRIMARY ARTIFACT: <report/profile/dashboard>
DECISION: <ship / rollback / escalate>

Execution drill pack 10

Use this pack to rehearse AI-for-VLSI decision making on ai-vlsi/training-data-pipeline/hyperparameters-and-schedulers/debug-playbook: metric framing, mechanism proof, hardware implications, and release safety.

Evidence checklist

• Metric context includes workload, dataset slice, and revision tags.

• Mechanism explanation links model behavior to observed outcome.

• Hardware/runtime feasibility is profiled, not assumed.

• Owner and rollback path are documented before rollout.

Review prompts

1. Which decision will this model output influence?

2. What is the first failing layer when metric regresses?

3. Which owner applies the smallest reversible fix?

4. What validation matrix is required before deployment?

Evidence capsule

AI-VLSI EVIDENCE CAPSULE 10

PATH: ai-vlsi/training-data-pipeline/hyperparameters-and-schedulers/debug-playbook
WORKLOAD SLICE: <name>
PRIMARY METRIC: <value/trend>
FIRST FAILING LAYER: <data/model/runtime/hardware>
OWNER: <name>
PRIMARY ARTIFACT: <report/profile/dashboard>
DECISION: <ship / rollback / escalate>

Execution drill pack 11

Use this pack to rehearse AI-for-VLSI decision making on ai-vlsi/training-data-pipeline/hyperparameters-and-schedulers/debug-playbook: metric framing, mechanism proof, hardware implications, and release safety.

Evidence checklist

• Metric context includes workload, dataset slice, and revision tags.

• Mechanism explanation links model behavior to observed outcome.

• Hardware/runtime feasibility is profiled, not assumed.

• Owner and rollback path are documented before rollout.

Review prompts

1. Which decision will this model output influence?

2. What is the first failing layer when metric regresses?

3. Which owner applies the smallest reversible fix?

4. What validation matrix is required before deployment?

Evidence capsule

AI-VLSI EVIDENCE CAPSULE 11

PATH: ai-vlsi/training-data-pipeline/hyperparameters-and-schedulers/debug-playbook
WORKLOAD SLICE: <name>
PRIMARY METRIC: <value/trend>
FIRST FAILING LAYER: <data/model/runtime/hardware>
OWNER: <name>
PRIMARY ARTIFACT: <report/profile/dashboard>
DECISION: <ship / rollback / escalate>

Execution drill pack 12

Use this pack to rehearse AI-for-VLSI decision making on ai-vlsi/training-data-pipeline/hyperparameters-and-schedulers/debug-playbook: metric framing, mechanism proof, hardware implications, and release safety.

Evidence checklist

• Metric context includes workload, dataset slice, and revision tags.

• Mechanism explanation links model behavior to observed outcome.

• Hardware/runtime feasibility is profiled, not assumed.

• Owner and rollback path are documented before rollout.

Review prompts

1. Which decision will this model output influence?

2. What is the first failing layer when metric regresses?

3. Which owner applies the smallest reversible fix?

4. What validation matrix is required before deployment?

Evidence capsule

AI-VLSI EVIDENCE CAPSULE 12

PATH: ai-vlsi/training-data-pipeline/hyperparameters-and-schedulers/debug-playbook
WORKLOAD SLICE: <name>
PRIMARY METRIC: <value/trend>
FIRST FAILING LAYER: <data/model/runtime/hardware>
OWNER: <name>
PRIMARY ARTIFACT: <report/profile/dashboard>
DECISION: <ship / rollback / escalate>

Execution drill pack 13

Use this pack to rehearse AI-for-VLSI decision making on ai-vlsi/training-data-pipeline/hyperparameters-and-schedulers/debug-playbook: metric framing, mechanism proof, hardware implications, and release safety.

Evidence checklist

• Metric context includes workload, dataset slice, and revision tags.

• Mechanism explanation links model behavior to observed outcome.

• Hardware/runtime feasibility is profiled, not assumed.

• Owner and rollback path are documented before rollout.

Review prompts

1. Which decision will this model output influence?

2. What is the first failing layer when metric regresses?

3. Which owner applies the smallest reversible fix?

4. What validation matrix is required before deployment?

Evidence capsule

AI-VLSI EVIDENCE CAPSULE 13

PATH: ai-vlsi/training-data-pipeline/hyperparameters-and-schedulers/debug-playbook
WORKLOAD SLICE: <name>
PRIMARY METRIC: <value/trend>
FIRST FAILING LAYER: <data/model/runtime/hardware>
OWNER: <name>
PRIMARY ARTIFACT: <report/profile/dashboard>
DECISION: <ship / rollback / escalate>

Principal AI-VLSI review addendum

Learning rate, batch size, and scheduler policy dominate training efficiency and final model quality under finite compute budget.

Metric: best validation score per compute hour, convergence speed, and run-to-run variance