Review the 70-chapter draft route · This is a review edition.
COURSE 38 OF 40 · PRACTICAL STARTER EDITION
AI Hardware & Acceleration: from zero toward mastery
AI hardware determines how quickly a computation can run and how much energy it consumes. CPUs, GPUs and specialized accelerators have different strengths depending on parallelism, memory and operation type.
Course roadmap
This course has 18 distinct, curated curriculum milestones. Only the first newly written chapter is included in this edition; milestones are topics to develop, not claims that every chapter is complete.
- 01Absolute beginnerCPU versus GPU workloads
- 02Absolute beginnerMatrix arithmetic costs
- 03Absolute beginnerMemory hierarchy
- 04FundamentalsVectorization
- 05FundamentalsGPU threads and warps
- 06FundamentalsKernel launch overhead
- 07IntermediateBatch size effects
- 08IntermediateTensor operations
- 09IntermediateMixed precision
- 10AdvancedQuantized computation
- 11AdvancedAccelerator memory limits
- 12AdvancedData transfer bottlenecks
- 13Professional practiceProfiling inference
- 14Professional practiceKernel fusion concepts
- 15Professional practiceCompilation and graph capture
- 16Expert and masteryThermal and power limits
- 17Expert and masteryBenchmark fairness
- 18Expert and masteryCapstone: optimize a measured inference workload
Existing 70-chapter navigation (not overwritten)
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