Power Virus and Thermal Stress Testing: Debug Playbook

Debug playbook

Debug Playbook for Power Virus and Thermal Stress Testing is anchored on Worst-case sustained power, hotspot temperature, throttling onset, and recovery behavior under stress kernels.. 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 / Power Virus and Thermal Stress Testing

1. Symptom
   - Failing metric: Worst-case sustained power, hotspot temperature, throttling onset, and recovery behavior under stress kernels.
   - 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: Power-virus workloads intentionally maximize switching activity across compute and memory fabrics to test package, cooling, and DVFS control margins. Thermal testing should include steady-state and transient bursts to capture hotspot migration, sensor lag, and control-loop stability. Bring-up teams compare measured envelopes against pre-silicon estimates to validate guardbands and identify hidden leakage or IR-drop sensitivities. Early stress characterization prevents field failures where real customer workloads combine high utilization with unfavorable ambient conditions.
   - 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: Thermal and power stress report with limits, throttle policy checks, and mitigation recommendations.
   - Required owners: power and thermal architect, silicon reliability engineer, board validation owner, firmware power-management owner
   - 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

Power Virus and Thermal Stress Testing 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.

Power-virus workloads intentionally maximize switching activity across compute and memory fabrics to test package, cooling, and DVFS control margins. Thermal testing should include steady-state and transient bursts to capture hotspot migration, sensor lag, and control-loop stability. Bring-up teams compare measured envelopes against pre-silicon estimates to validate guardbands and identify hidden leakage or IR-drop sensitivities. Early stress characterization prevents field failures where real customer workloads combine high utilization with unfavorable ambient conditions. A useful explanation always ties observed symptom to a repeatable path where useful work was blocked, delayed, or diluted by overhead.

Use Worst-case sustained power, hotspot temperature, throttling onset, and recovery behavior under stress kernels. as an alarm, then anchor action using hard evidence such as Thermal and power stress report with limits, throttle policy checks, and mitigation recommendations..

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.