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ONNX & Runtime Engines: Review Checklist

Review Checklist for ONNX & Runtime Engines.

Review checklist

Review Checklist for ONNX & Runtime Engines focuses on model conversion success rate, runtime compatibility, and inference latency. The goal is to connect model behavior to hardware-aware decisions and operational risk.

  • Workload scope and acceptance KPI are explicitly named.

  • Data split and leakage audit is attached.

  • Model metric and deployment metric correlation is shown.

  • Runtime/hardware profile is included for feasibility.

  • Rollback conditions and owner matrix are clear.

  • Owners signed: deployment engineer, runtime owner, SDK team.

Signoff ownership

diagram
OWNERSHIP MAP — ONNX & Runtime Engines

artifact focus         owner
------------------     ----------------------------
modeling          deployment engineer
architecture      runtime owner
integration       SDK team

Production issues happen when ownership is assumed, not declared.

AI-VLSI deep dive

Deployment quality is determined by compatibility, observability, and rollback discipline.

Concept diagram

diagram
DEPLOYMENT LOOP

model export -> runtime integration -> production monitor -> retrain/rollback

Metric graph

diagram
DEPLOYMENT HEALTH

conversion pass rate    ████████
latency SLA pass        ███████
drift incidents         ██

Reports and artifacts

  • ONNX conversion report

  • quantized eval sheet

  • stack latency breakdown

  • drift incident tracker

Mini case study

A runtime operator gap was caught pre-release by compatibility checks and shadow deployment.

Debug branches

  • Validate operator coverage

  • Trace firmware-driver latency

  • Check drift thresholds and rollback path

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/edge-deployment-mlops/onnx-and-runtime-engines/checklist: 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

diagram
AI-VLSI EVIDENCE CAPSULE 1

PATH: ai-vlsi/edge-deployment-mlops/onnx-and-runtime-engines/checklist
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/edge-deployment-mlops/onnx-and-runtime-engines/checklist: 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

diagram
AI-VLSI EVIDENCE CAPSULE 2

PATH: ai-vlsi/edge-deployment-mlops/onnx-and-runtime-engines/checklist
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/edge-deployment-mlops/onnx-and-runtime-engines/checklist: 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

diagram
AI-VLSI EVIDENCE CAPSULE 3

PATH: ai-vlsi/edge-deployment-mlops/onnx-and-runtime-engines/checklist
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/edge-deployment-mlops/onnx-and-runtime-engines/checklist: 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

diagram
AI-VLSI EVIDENCE CAPSULE 4

PATH: ai-vlsi/edge-deployment-mlops/onnx-and-runtime-engines/checklist
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/edge-deployment-mlops/onnx-and-runtime-engines/checklist: 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

diagram
AI-VLSI EVIDENCE CAPSULE 5

PATH: ai-vlsi/edge-deployment-mlops/onnx-and-runtime-engines/checklist
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/edge-deployment-mlops/onnx-and-runtime-engines/checklist: 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

diagram
AI-VLSI EVIDENCE CAPSULE 6

PATH: ai-vlsi/edge-deployment-mlops/onnx-and-runtime-engines/checklist
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/edge-deployment-mlops/onnx-and-runtime-engines/checklist: 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

diagram
AI-VLSI EVIDENCE CAPSULE 7

PATH: ai-vlsi/edge-deployment-mlops/onnx-and-runtime-engines/checklist
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/edge-deployment-mlops/onnx-and-runtime-engines/checklist: 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

diagram
AI-VLSI EVIDENCE CAPSULE 8

PATH: ai-vlsi/edge-deployment-mlops/onnx-and-runtime-engines/checklist
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/edge-deployment-mlops/onnx-and-runtime-engines/checklist: 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

diagram
AI-VLSI EVIDENCE CAPSULE 9

PATH: ai-vlsi/edge-deployment-mlops/onnx-and-runtime-engines/checklist
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/edge-deployment-mlops/onnx-and-runtime-engines/checklist: 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

diagram
AI-VLSI EVIDENCE CAPSULE 10

PATH: ai-vlsi/edge-deployment-mlops/onnx-and-runtime-engines/checklist
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/edge-deployment-mlops/onnx-and-runtime-engines/checklist: 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

diagram
AI-VLSI EVIDENCE CAPSULE 11

PATH: ai-vlsi/edge-deployment-mlops/onnx-and-runtime-engines/checklist
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/edge-deployment-mlops/onnx-and-runtime-engines/checklist: 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

diagram
AI-VLSI EVIDENCE CAPSULE 12

PATH: ai-vlsi/edge-deployment-mlops/onnx-and-runtime-engines/checklist
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/edge-deployment-mlops/onnx-and-runtime-engines/checklist: 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

diagram
AI-VLSI EVIDENCE CAPSULE 13

PATH: ai-vlsi/edge-deployment-mlops/onnx-and-runtime-engines/checklist
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

Standard model interchange and runtime engines bridge training frameworks to production execution stacks on edge and embedded targets.

Metric: model conversion success rate, runtime compatibility, and inference latency

Practice this lesson