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Course Outline
Overview of Custom Operator Development
- Rationale for developing custom operators: applicable scenarios and technical constraints
- CANN runtime architecture and mechanisms for operator integration
- Summary of TBE, TIK, and TVM within the Huawei AI infrastructure
Implementation of Low-Level Operator Logic Using TIK
- Analysis of the TIK programming model and supported application interfaces
- Memory allocation strategies and tiling techniques within TIK
- Procedures for constructing, compiling, and registering custom operators with CANN
Verification and Validation of Custom Operators
- Execution of unit and integration tests for operators within the computational graph
- Identification and resolution of performance bottlenecks at the kernel level
- Visualization techniques for operator execution flows and buffer states
Scheduler Design and Optimization via TVM
- Role of TVM as a compiler framework for tensor operations
- Construction of scheduling policies for custom operators using TVM
- Hyperparameter tuning, performance benchmarking, and code generation for Ascend hardware
Integration with Deep Learning Frameworks and Models
- Registration of custom operators for compatibility with MindSpore and ONNX standards
- Assurance of model fidelity and management of fallback mechanisms
- Support for multi-operator computational graphs featuring mixed-precision execution
Case Studies and Targeted Optimizations
- Case study: optimizing convolution operations for small input dimensions
- Case study: memory-efficient optimization of attention mechanisms
- Established protocols for deploying custom operators across diverse device configurations
Conclusion and Strategic Recommendations for government initiatives
Requirements
- Comprehensive understanding of artificial intelligence model architectures and operator-level computational processes
- Practical proficiency in Python programming within Linux-based development ecosystems
- Working knowledge of neural network compilers and graph-based optimization techniques
Audience
- Compiler engineers engaged in the development of AI toolchains for government applications
- Systems developers specializing in low-level performance optimization for artificial intelligence systems
- Engineers responsible for creating custom operations or supporting emerging AI workload requirements
14 Hours
