Debug Playbook for Forward Pass Mechanics focuses on layer-wise latency, memory traffic, and arithmetic intensity. 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 — Forward Pass Mechanics
layer-wise latency, memory traffic, and arithmetic intensity 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.Freeze dataset, model, runtime, and hardware revision tags.
Isolate first failing workload slice and metric shift.
Classify issue: data drift, model behavior, runtime mismatch, or hardware bottleneck.
Prove hypothesis with one reduced reproducible experiment.
Patch smallest owner-controlled fix.
Re-run full quality + performance + stability validation.
AI-VLSI REVIEW MEMO — Neural Networks Basics / Forward Pass Mechanics
1. Symptom
- Watched metric: layer-wise latency, memory traffic, and arithmetic intensity
- Failing workload slice: <name>
- Failing layer: <data/model/runtime/hardware>
- Revision tags: <dataset, model, runtime, hardware>
2. Mechanism hypothesis
- Primary mechanism: The forward pass maps inputs to outputs through layer execution order, defining runtime compute/memory behavior and accelerator bottlenecks.
- 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: layer execution trace, roofline worksheet, and runtime profile
- Required owners: runtime engineer, accelerator architect, compiler owner
- Final decision: ship, rollback, bounded pilot, or escalateNetwork reliability depends on stable activations, gradients, and compute-aware architecture choices.
NETWORK LOOP
topology -> forward -> backward -> update -> evaluateTRAINING STABILITY
exploding gradients ███
vanishing gradients ████
stable runs █████████activation histogram
gradient norm trend
layer profile
ablation log
Adding normalization reduced training variance and cut rerun churn across teams.
Inspect activation saturation
Run gradient checks
Profile per-layer cost
Ask: what evidence connects this ML claim to a concrete VLSI workflow decision and owner signoff?
Every AI claim should map to a measurable engineering outcome.
Validate both model quality and hardware/runtime feasibility before adoption.
Optimizing benchmark metrics that do not correlate with signoff goals.
Ignoring data drift and calibration after deployment.
Shipping ML workflows without clear rollback ownership.
Use this pack to rehearse AI-for-VLSI decision making on ai-vlsi/neural-networks-basics/forward-pass-mechanics/debug-playbook: metric framing, mechanism proof, hardware implications, and release safety.
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.
Which decision will this model output influence?
What is the first failing layer when metric regresses?
Which owner applies the smallest reversible fix?
What validation matrix is required before deployment?
AI-VLSI EVIDENCE CAPSULE 1
PATH: ai-vlsi/neural-networks-basics/forward-pass-mechanics/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>Use this pack to rehearse AI-for-VLSI decision making on ai-vlsi/neural-networks-basics/forward-pass-mechanics/debug-playbook: metric framing, mechanism proof, hardware implications, and release safety.
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.
Which decision will this model output influence?
What is the first failing layer when metric regresses?
Which owner applies the smallest reversible fix?
What validation matrix is required before deployment?
AI-VLSI EVIDENCE CAPSULE 2
PATH: ai-vlsi/neural-networks-basics/forward-pass-mechanics/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>Use this pack to rehearse AI-for-VLSI decision making on ai-vlsi/neural-networks-basics/forward-pass-mechanics/debug-playbook: metric framing, mechanism proof, hardware implications, and release safety.
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.
Which decision will this model output influence?
What is the first failing layer when metric regresses?
Which owner applies the smallest reversible fix?
What validation matrix is required before deployment?
AI-VLSI EVIDENCE CAPSULE 3
PATH: ai-vlsi/neural-networks-basics/forward-pass-mechanics/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>Use this pack to rehearse AI-for-VLSI decision making on ai-vlsi/neural-networks-basics/forward-pass-mechanics/debug-playbook: metric framing, mechanism proof, hardware implications, and release safety.
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.
Which decision will this model output influence?
What is the first failing layer when metric regresses?
Which owner applies the smallest reversible fix?
What validation matrix is required before deployment?
AI-VLSI EVIDENCE CAPSULE 4
PATH: ai-vlsi/neural-networks-basics/forward-pass-mechanics/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>Use this pack to rehearse AI-for-VLSI decision making on ai-vlsi/neural-networks-basics/forward-pass-mechanics/debug-playbook: metric framing, mechanism proof, hardware implications, and release safety.
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.
Which decision will this model output influence?
What is the first failing layer when metric regresses?
Which owner applies the smallest reversible fix?
What validation matrix is required before deployment?
AI-VLSI EVIDENCE CAPSULE 5
PATH: ai-vlsi/neural-networks-basics/forward-pass-mechanics/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>Use this pack to rehearse AI-for-VLSI decision making on ai-vlsi/neural-networks-basics/forward-pass-mechanics/debug-playbook: metric framing, mechanism proof, hardware implications, and release safety.
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.
Which decision will this model output influence?
What is the first failing layer when metric regresses?
Which owner applies the smallest reversible fix?
What validation matrix is required before deployment?
AI-VLSI EVIDENCE CAPSULE 6
PATH: ai-vlsi/neural-networks-basics/forward-pass-mechanics/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>Use this pack to rehearse AI-for-VLSI decision making on ai-vlsi/neural-networks-basics/forward-pass-mechanics/debug-playbook: metric framing, mechanism proof, hardware implications, and release safety.
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.
Which decision will this model output influence?
What is the first failing layer when metric regresses?
Which owner applies the smallest reversible fix?
What validation matrix is required before deployment?
AI-VLSI EVIDENCE CAPSULE 7
PATH: ai-vlsi/neural-networks-basics/forward-pass-mechanics/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>Use this pack to rehearse AI-for-VLSI decision making on ai-vlsi/neural-networks-basics/forward-pass-mechanics/debug-playbook: metric framing, mechanism proof, hardware implications, and release safety.
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.
Which decision will this model output influence?
What is the first failing layer when metric regresses?
Which owner applies the smallest reversible fix?
What validation matrix is required before deployment?
AI-VLSI EVIDENCE CAPSULE 8
PATH: ai-vlsi/neural-networks-basics/forward-pass-mechanics/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>Use this pack to rehearse AI-for-VLSI decision making on ai-vlsi/neural-networks-basics/forward-pass-mechanics/debug-playbook: metric framing, mechanism proof, hardware implications, and release safety.
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.
Which decision will this model output influence?
What is the first failing layer when metric regresses?
Which owner applies the smallest reversible fix?
What validation matrix is required before deployment?
AI-VLSI EVIDENCE CAPSULE 9
PATH: ai-vlsi/neural-networks-basics/forward-pass-mechanics/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>Use this pack to rehearse AI-for-VLSI decision making on ai-vlsi/neural-networks-basics/forward-pass-mechanics/debug-playbook: metric framing, mechanism proof, hardware implications, and release safety.
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.
Which decision will this model output influence?
What is the first failing layer when metric regresses?
Which owner applies the smallest reversible fix?
What validation matrix is required before deployment?
AI-VLSI EVIDENCE CAPSULE 10
PATH: ai-vlsi/neural-networks-basics/forward-pass-mechanics/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>Use this pack to rehearse AI-for-VLSI decision making on ai-vlsi/neural-networks-basics/forward-pass-mechanics/debug-playbook: metric framing, mechanism proof, hardware implications, and release safety.
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.
Which decision will this model output influence?
What is the first failing layer when metric regresses?
Which owner applies the smallest reversible fix?
What validation matrix is required before deployment?
AI-VLSI EVIDENCE CAPSULE 11
PATH: ai-vlsi/neural-networks-basics/forward-pass-mechanics/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>Use this pack to rehearse AI-for-VLSI decision making on ai-vlsi/neural-networks-basics/forward-pass-mechanics/debug-playbook: metric framing, mechanism proof, hardware implications, and release safety.
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.
Which decision will this model output influence?
What is the first failing layer when metric regresses?
Which owner applies the smallest reversible fix?
What validation matrix is required before deployment?
AI-VLSI EVIDENCE CAPSULE 12
PATH: ai-vlsi/neural-networks-basics/forward-pass-mechanics/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>Use this pack to rehearse AI-for-VLSI decision making on ai-vlsi/neural-networks-basics/forward-pass-mechanics/debug-playbook: metric framing, mechanism proof, hardware implications, and release safety.
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.
Which decision will this model output influence?
What is the first failing layer when metric regresses?
Which owner applies the smallest reversible fix?
What validation matrix is required before deployment?
AI-VLSI EVIDENCE CAPSULE 13
PATH: ai-vlsi/neural-networks-basics/forward-pass-mechanics/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>The forward pass maps inputs to outputs through layer execution order, defining runtime compute/memory behavior and accelerator bottlenecks.
Metric: layer-wise latency, memory traffic, and arithmetic intensity
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