Tensor Cores & Sparse Compute

Section goal

A hardware-software view of tensor core execution, mixed-precision matmul pipelines, and sparsity techniques that change effective throughput, memory pressure, and model quality.

How to study this section

1. Start with each topic hub and restate the mechanism in your own words.

2. Use reports and debug pages to separate symptoms from root causes.

3. Practice worked examples and interview drills under fixed metadata.

4. Close with checklist and silicon impact before making release claims.

Topics

1. tensor-core-mechanics/ - Tensor Core Mechanics: Tiles, Pipelines, and Data Movement

2. mixed-precision-matmul/ - Mixed-Precision Matmul: Throughput Gains with Accuracy Controls

3. structured-sparsity/ - Structured Sparsity: Hardware-Friendly Zero Patterns

4. unstructured-sparsity-tradeoffs/ - Unstructured Sparsity Tradeoffs: Flexibility vs Execution Efficiency

Related topics

• AI Accelerator course hub

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?