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Duration 14 hours
Course Outline
Overview of CANN Optimization Capabilities
- How inference performance is managed within CANN.
- Optimization targets for edge and embedded AI systems.
- Comprehending AI Core utilization and memory allocation.
Utilizing Graph Engine for Analysis
- Introduction to the Graph Engine and its execution pipeline.
- Visualizing operator graphs and runtime metrics.
- Modifying computational graphs to enhance optimization.
Profiling Tools and Performance Metrics
- Using the CANN Profiling Tool (profiler) for workload analysis.
- Analyzing kernel execution times and identifying bottlenecks.
- Profiling memory access patterns and tiling strategies.
Custom Operator Development with TIK
- Overview of TIK and its operator programming model.
- Implementing custom operators using the TIK DSL.
- Testing and benchmarking operator performance.
Advanced Operator Optimization with TVM
- Introduction to TVM integration with CANN.
- Auto-tuning strategies for computational graphs.
- Determining when and how to switch between TVM and TIK.
Memory Optimization Techniques
- Managing memory layouts and buffer placement.
- Techniques to minimize on-chip memory consumption.
- Best practices for asynchronous execution and data reuse.
Real-World Deployment and Case Studies
- Case study: Performance tuning for a smart city camera pipeline.
- Case study: Optimizing the inference stack for autonomous vehicles.
- Guidelines for iterative profiling and continuous improvement.
Summary and Next Steps
Requirements
- A solid understanding of deep learning model architectures and training workflows.
- Practical experience in model deployment using CANN, TensorFlow, or PyTorch.
- Proficiency in Linux CLI, shell scripting, and Python programming.
Audience
- AI performance engineers.
- Inference optimization specialists.
- Developers working with edge AI or real-time systems.
