Post-Silicon Accelerator Debug: Debug Playbook

Debug playbook

Debug Playbook for Post-Silicon Accelerator Debug is anchored on Mean time to isolate root cause from lab failure to actionable fix across hardware, firmware, and compiler layers.. Convert measurements into mechanism-backed decisions with clear owner accountability.

1. Freeze workload seed, model revision, and execution environment.

2. Locate first persistent stage where metrics diverge.

3. Classify dominant mechanism: compute, memory, scheduling, precision, or thermal.

4. Build one focused reproducer and apply one bounded fix.

5. Re-run full correctness, quality, and performance matrix.

Review memo template

ACCELERATOR REVIEW MEMO - Verification & Silicon Bring-up / Post-Silicon Accelerator Debug

1. Symptom
   - Failing metric: Mean time to isolate root cause from lab failure to actionable fix across hardware, firmware, and compiler layers.
   - Workload or traffic slice: <name>
   - First failing layer or stage: <operator, schedule, memory, runtime>
   - Build and runtime tags: <compiler/firmware/runtime/hardware>

2. Mechanism hypothesis
   - Primary mechanism: Post-silicon debug requires reproducible failure capture, observability hooks, and hypothesis-driven triage across the stack. Teams combine trace buffers, scan and debug ports, firmware logs, and workload minimization to localize issues without losing timing context. Cross-functional debug is essential because a symptom in model output can originate from protocol timing, numeric format handling, or software scheduling assumptions. Institutionalizing debug playbooks and bug taxonomies improves turnaround for both immediate workarounds and long-term design fixes.
   - Competing hypotheses: <dataflow mismatch, memory stalls, precision drift, thermal limits>
   - Missing evidence: <counter packet, trace, replay, signoff data>

3. Proposed action
   - Smallest reversible change: <mapping/runtime/policy/config>
   - Expected movement: <throughput, p99 latency, perf-per-watt>
   - Regression risk: correctness, quality, thermal, software compatibility

4. Signoff
   - Required artifact: Bring-up debug runbook with triage checklist, trace requirements, and escalation paths.
   - Required owners: post-silicon validation lead, debug tools engineer, firmware lead, compiler and kernel lead
   - Final decision: ship, bounded rollout, rollback, or escalate

AI accelerator deep dive

Bring-up speed and correctness depend on designed-in observability and replayable debug flow.

Concept diagram

BRING-UP EVIDENCE LOOP

failure symptom -> trace packet -> replay -> isolate root cause -> bounded fix

Metric graph

OBSERVABILITY VALUE

directed tests only      ██████████
plus counters            ███████
plus trace and replay    ███

Metrics and artifacts to collect

• counter completeness

• trace trigger coverage

• replay success rate

• escape-risk trend

Mini case study

A silicon-only regression closed quickly because trace identity and counter alignment were planned before tapeout.

Debug branches

• Start from first failing trace window

• Align software and hardware timestamps

• Demand reversible owner fix before signoff

Senior review question

Ask: which first-principles bottleneck class explains the symptom, and what artifact proves it reproducibly?

Key takeaways

• Tie every accelerator claim to a reproducible workload slice and one primary metric trend.

• Prefer bounded fixes with clear owner and rollback boundary over broad tuning bundles.

Common pitfalls

• Optimizing synthetic kernels without production-shape validation.

• Reading average latency while ignoring p95 and p99 behavior.

• Declaring sparse or precision wins without fallback and quality evidence.

Principal accelerator review addendum

Post-Silicon Accelerator Debug should be framed as a full-system behavior, not an isolated kernel trick. Production outcomes are set by model shape mix, compiler choices, runtime queueing policy, memory hierarchy limits, and silicon delivery margins.

Post-silicon debug requires reproducible failure capture, observability hooks, and hypothesis-driven triage across the stack. Teams combine trace buffers, scan and debug ports, firmware logs, and workload minimization to localize issues without losing timing context. Cross-functional debug is essential because a symptom in model output can originate from protocol timing, numeric format handling, or software scheduling assumptions. Institutionalizing debug playbooks and bug taxonomies improves turnaround for both immediate workarounds and long-term design fixes. A useful explanation always ties observed symptom to a repeatable path where useful work was blocked, delayed, or diluted by overhead.

Use Mean time to isolate root cause from lab failure to actionable fix across hardware, firmware, and compiler layers. as an alarm, then anchor action using hard evidence such as Bring-up debug runbook with triage checklist, trace requirements, and escalation paths..

Signoff strength comes from proving first-silicon observability and reproducible closure paths. Senior reviews expect a chain of proof: workload intent -> mapping -> hardware behavior -> product impact.

Use this addendum to force explicit owner assignment, bounded fixes, and reproducible evidence before declaring closure.