Mixed-Precision Matmul: Throughput Gains with Accuracy Controls: Inputs and Outputs

Inputs and outputs contract

Inputs and Outputs for Mixed-Precision Matmul: Throughput Gains with Accuracy Controls is anchored on Tokens or inferences per second at fixed quality target and overflow or underflow incidence per training or inference step.. Convert measurements into mechanism-backed decisions with clear owner accountability.

INPUTS
  - workload profile and SLA target
  - model precision and quality thresholds
  - compiler/runtime/firmware metadata
  - hardware operating envelope assumptions

OUTPUTS
  - evidence-backed bottleneck classification
  - owner-signed mitigation proposal
  - validation matrix with rollback triggers
  - release recommendation

Ownership split

OWNERSHIP LAYERS - Mixed-Precision Matmul: Throughput Gains with Accuracy Controls

+----------------------+--------------------------------+--------------------------------+
| Team                 | Primary responsibility         | Closure artifact               |
+----------------------+--------------------------------+--------------------------------+
| ML systems architect | mechanism and architecture intent| design rationale + tradeoffs   |
| numerical methods owner | mapping, runtime, and execution   | profile traces + bottleneck map|
| compiler quantization engineer | correctness, risk, and signoff    | test report + closure memo     |
+----------------------+--------------------------------+--------------------------------+

AI accelerator deep dive

Sparse and mixed-precision wins require stable compiler lowering and runtime support coverage.

Concept diagram

SPARSE TENSOR EXECUTION

model graph -> compiler lower -> sparse or dense kernel path -> runtime scheduling -> SLA outcome

Metric graph

SPARSE REALITY CHECK

nominal sparsity      ████████████
real speedup          ██████
fallback overhead     █████

Metrics and artifacts to collect

• tensor-core occupancy

• fallback kernel rate

• sparse metadata overhead

• quality guardrail drift

Mini case study

Structured sparsity improved one layer family while unsupported operators forced dense fallbacks elsewhere.

Debug branches

• Track dense fallback counters

• Audit sparse-format conversions

• Check precision policy with quality gates

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.

Handoff explanation

Inputs should include workload profile, model revision, compiler/runtime versions, and platform power mode.

Outputs must include actionable interpretation of Tokens or inferences per second at fixed quality target and overflow or underflow incidence per training or inference step., required artifacts (Precision policy sheet mapping operators to compute and accumulation formats with quality guardrails.), owner, and validation scope.

The ideal handoff packet is reproducible: fixed seeds, explicit baseline, and rejected alternatives.