Building Custom AI Operators with CANN TIK and TVM Training Course

Introduction to Custom Operator Development

• Rationale for building custom operators: use cases and constraints
• Architecture of the CANN runtime and operator integration points
• Overview of TBE, TIK, and TVM within the Huawei AI ecosystem

Low-Level Operator Programming with TIK

• Exploring the TIK programming model and supported APIs
• Memory management and tiling strategies in TIK
• Creating, compiling, and registering custom operations with CANN

Testing and Validating Custom Operations

• Unit and integration testing of operations within the graph
• Diagnosing kernel-level performance bottlenecks
• Visualizing operation execution and buffer behavior

Scheduling and Optimization via TVM

• Introduction to TVM as a compiler for tensor operations
• Developing schedules for custom operations in TVM
• TVM tuning, benchmarking, and code generation tailored for Ascend

Framework and Model Integration

• Registering custom operations for MindSpore and ONNX
• Verifying model integrity and fallback mechanisms
• Handling multi-operator graphs with mixed precision support

Case Studies and Specialized Optimizations

• Case study: High-efficiency convolution for small input shapes
• Case study: Memory-aware attention operator optimization
• Best practices for deploying custom operations across various devices

Summary and Next Steps