Performance Optimization on Ascend, Biren, and Cambricon

• Experience working with AI model training or deployment pipelines
• Understanding of GPU/MLU compute principles and model optimization
• Basic familiarity with performance profiling tools and metrics

Audience

• Performance engineers
• Machine learning infrastructure teams
• AI system architects

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 optimize 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 optimizations.
• Tune deployment pipelines to improve throughput and latency.

Format of the Course

• Interactive lecture and discussion.
• Hands-on use of profiling and optimization tools on each platform.
• Guided exercises focused on practical tuning scenarios.

Course Customization Options

• To request a customized training for this course based on your performance environment or model type, please contact us to arrange.

Performance Concepts and Metrics

• Latency, throughput, power usage, resource utilization
• 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 Optimization

• Graph pruning and quantization strategies
• Operator fusion and computational graph restructuring
• Input size standardization and batch tuning

Memory and Kernel Optimization

• Optimizing 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