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Course Outline
Overview of CANN Optimization Capabilities
- How inference performance is managed in CANN
- Optimization objectives for edge and embedded AI systems
- Understanding AI Core utilization and memory allocation
Using Graph Engine for Analysis
- Introduction to the Graph Engine and its execution pipeline
- Visualizing operator graphs and runtime metrics
- Modifying computational graphs for optimization
Profiling Tools and Performance Metrics
- Utilizing the CANN Profiling Tool (profiler) for workload analysis
- Analyzing kernel execution time and identifying bottlenecks
- Memory access profiling and tiling strategies
Custom Operator Development with TIK
- Overview of TIK and the operator programming model
- Implementing a custom operator 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
- When and how to switch between TVM and TIK
Memory Optimization Techniques
- Managing memory layout and buffer placement
- Techniques to reduce on-chip memory consumption
- Best practices for asynchronous execution and reuse
Real-World Deployment and Case Studies
- Case study: performance tuning for smart city camera pipeline
- Case study: optimizing autonomous vehicle inference stack
- Guidelines for iterative profiling and continuous improvement, specifically tailored for government applications
Summary and Next Steps
Requirements
- Demonstrated knowledge of deep learning model architectures and training workflows for government applications
- Experience in deploying models using CANN, TensorFlow, or PyTorch in a government context
- Proficiency with Linux command-line interface (CLI), shell scripting, and Python programming for government systems
Audience
- AI performance engineers supporting government initiatives
- Inference optimization specialists working on government projects
- Developers focused on edge AI or real-time systems for government use
14 Hours
