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Course Outline
Overview of CANN Optimization Capabilities
- Understanding how inference performance is managed within CANN.
- Defining optimization objectives for edge and embedded AI systems.
- Comprehending AI Core utilization and memory allocation strategies.
Leveraging the Graph Engine for Analysis
- Introduction to the Graph Engine and its execution pipeline.
- Visualizing operator graphs and runtime metrics.
- Adjusting computational graphs to achieve optimization.
Profiling Tools and Performance Metrics
- Utilizing the CANN Profiling Tool for workload analysis.
- Evaluating kernel execution time and identifying bottlenecks.
- Profiling memory access and implementing tiling strategies.
Custom Operator Development with TIK
- Exploring the TIK overview and operator programming model.
- Implementing custom operators using the TIK DSL.
- Conducting testing and benchmarking of operator performance.
Advanced Operator Optimization with TVM
- Introduction to TVM integration with CANN.
- Employing auto-tuning strategies for computational graphs.
- Determining when and how to transition between TVM and TIK.
Memory Optimization Techniques
- Managing memory layouts and buffer placement.
- Applying techniques to reduce on-chip memory consumption.
- Adopting best practices for asynchronous execution and resource 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
- Comprehensive knowledge of deep learning model architectures and training workflows.
- Practical experience with model deployment via CANN, TensorFlow, or PyTorch.
- Proficiency in Linux CLI, shell scripting, and Python programming.
Target Audience
- AI performance engineers.
- Specialists in inference optimization.
- Developers working on edge AI or real-time systems.
14 Hours
