Performance Optimization on Ascend, Biren, and Cambricon Training Course
Ascend, Biren, and Cambricon are leading AI hardware platforms in China, each offering unique acceleration and profiling tools for production-scale AI workloads.
This instructor-led, live training (online or onsite) is aimed at advanced-level AI infrastructure and performance engineers who wish to optimise model inference and training workflows across multiple Chinese AI chip platforms.
By the end of this training, participants will be able to:
- Benchmark models on Ascend, Biren, and Cambricon platforms.
- Identify system bottlenecks and memory/compute inefficiencies.
- Apply graph-level, kernel-level, and operator-level optimisations.
- Tune deployment pipelines to improve throughput and latency.
Format of the Course
- Interactive lecture and discussion.
- Hands-on use of profiling and optimisation tools on each platform.
- Guided exercises focused on practical tuning scenarios.
Course Customisation Options
- To request a customised training for this course based on your performance environment or model type, please contact us to arrange.
Course Outline
Performance Concepts and Metrics
- Latency, throughput, power usage, resource utilisation
- System vs model-level bottlenecks
- Profiling for inference vs training
Profiling on Huawei Ascend
- Using CANN Profiler and MindInsight
- Kernel and operator diagnostics
- Offload patterns and memory mapping
Profiling on Biren GPU
- Biren SDK performance monitoring features
- Kernel fusion, memory alignment, and execution queues
- Power and temperature-aware profiling
Profiling on Cambricon MLU
- BANGPy and Neuware performance tools
- Kernel-level visibility and log interpretation
- MLU profiler integration with deployment frameworks
Graph and Model-Level Optimisation
- Graph pruning and quantisation strategies
- Operator fusion and computational graph restructuring
- Input size standardisation and batch tuning
Memory and Kernel Optimisation
- Optimising memory layout and reuse
- Efficient buffer management across chipsets
- Kernel-level tuning techniques per platform
Cross-Platform Best Practices
- Performance portability: abstraction strategies
- Building shared tuning pipelines for multi-chip environments
- Example: tuning an object detection model across Ascend, Biren, and MLU
Summary and Next Steps
Requirements
- Experience working with AI model training or deployment pipelines
- Understanding of GPU/MLU compute principles and model optimisation
- Basic familiarity with performance profiling tools and metrics
Audience
- Performance engineers
- Machine learning infrastructure teams
- AI system architects
Open Training Courses require 5+ participants.
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