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Course Outline
Overview of CANN Optimization Capabilities
- Mechanisms for handling inference performance within CANN
- Key optimization objectives for edge and embedded AI systems
- Insights into AI Core utilization and memory allocation strategies
Utilizing Graph Engine for Analysis
- Introduction to the Graph Engine and its execution pipeline
- Visualization of operator graphs alongside runtime metrics
- Modifying computational graphs to drive optimization
Profiling Tools and Performance Metrics
- Employing the CANN Profiling Tool (profiler) for in-depth workload analysis
- Examining kernel execution times to identify bottlenecks
- Profiling memory access patterns and exploring tiling strategies
Custom Operator Development with TIK
- An overview of TIK and its operator programming model
- Implementing custom operators using the TIK DSL
- Testing and benchmarking operator performance metrics
Advanced Operator Optimization with TVM
- Introduction to integrating TVM with CANN
- Auto-tuning strategies applied to computational graphs
- Determining when and how to switch between TVM and TIK
Memory Optimization Techniques
- Managing memory layouts and buffer placement effectively
- Strategies 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 smart city camera pipelines
- Case study: Optimizing inference stacks for autonomous vehicles
- Guidelines for iterative profiling and sustained improvement
Summary and Next Steps
Requirements
- A solid command 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
Target Audience
- AI performance engineers
- Inference optimization specialists
- Developers engaged with edge AI or real-time systems
14 Hours
