Optimizing Neural Network Performance with CANN SDK

• Strong understanding of deep learning model architectures and training workflows
• Experience with model deployment using CANN, TensorFlow, or PyTorch
• Familiarity with Linux CLI, shell scripting, and Python programming

Audience

• AI performance engineers
• Inference optimization specialists
• Developers working with edge AI or real-time systems

CANN SDK (Compute Architecture for Neural Networks) is Huawei’s AI compute foundation that allows developers to fine-tune and optimize the performance of deployed neural networks on Ascend AI processors.

This instructor-led, live training (online or onsite) is aimed at advanced-level AI developers and system engineers who wish to optimize inference performance using CANN’s advanced toolset, including the Graph Engine, TIK, and custom operator development.

By the end of this training, participants will be able to:

• Understand CANN's runtime architecture and performance lifecycle.
• Use profiling tools and Graph Engine for performance analysis and optimization.
• Create and optimize custom operators using TIK and TVM.
• Resolve memory bottlenecks and improve model throughput.

Format of the Course

• Interactive lecture and discussion.
• Hands-on labs with real-time profiling and operator tuning.
• Optimization exercises using edge-case deployment examples.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Overview of CANN Optimization Capabilities

• How inference performance is handled in CANN
• Optimization goals for edge and embedded AI systems
• Understanding AI Core utilization and memory allocation

Using Graph Engine for Analysis

• Introduction to the Graph Engine and execution pipeline
• Visualizing operator graphs and runtime metrics
• Modifying computational graphs for optimization

Profiling Tools and Performance Metrics

• Using CANN Profiling Tool (profiler) for workload analysis
• Analyzing kernel execution time and bottlenecks
• Memory access profiling and tiling strategies

Custom Operator Development with TIK

• Overview of TIK and operator programming model
• Implementing a custom operator using TIK DSL
• Testing and benchmarking operator performance

Advanced Operator Optimization with TVM

• Intro 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

Summary and Next Steps