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Course Outline
Overview of CANN Optimization Capabilities
- Understanding how inference performance is managed within CANN.
- Defining optimization goals for edge and embedded AI systems.
- Gaining insight into AI Core utilization and memory allocation strategies.
Utilizing the Graph Engine for Analysis
- Introduction to the Graph Engine and its execution pipeline.
- Visualizing operator graphs and runtime metrics.
- Modifying computational graphs to achieve optimal performance.
Profiling Tools and Performance Metrics
- Employing the CANN Profiling Tool (profiler) for workload analysis.
- Analyzing kernel execution times and identifying bottlenecks.
- Conducting memory access profiling and exploring tiling strategies.
Custom Operator Development with TIK
- Overview of TIK and its operator programming model.
- Implementing custom operators using TIK DSL.
- Testing and benchmarking operator performance.
Advanced Operator Optimization with TVM
- Introduction to TVM integration with CANN.
- Auto-tuning strategies for computational graphs.
- Guidance on when and how to switch between TVM and TIK.
Memory Optimization Techniques
- Managing memory layout and buffer placement.
- Strategies to reduce on-chip memory consumption.
- Best practices for asynchronous execution and resource reuse.
Real-World Deployment and Case Studies
- Case study: Performance tuning for smart city camera pipelines.
- Case study: Optimizing the inference stack for autonomous vehicles.
- Guidelines for iterative profiling and continuous improvement.
Summary and Next Steps
Requirements
- Proficient knowledge of deep learning model architectures and training workflows.
- Hands-on experience deploying models using CANN, TensorFlow, or PyTorch.
- Comfort with Linux CLI, shell scripting, and Python programming.
Target Audience
- AI performance engineers.
- Specialists in inference optimization.
- Developers focused on edge AI or real-time systems.
14 Hours
