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 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.

This course is available as onsite live training in Ireland or online live training.

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Course Outline

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

Requirements

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

21 Hours

Delivery Options

Private Group Training

Our identity is rooted in delivering exactly what our clients need.

Pre-course call with your trainer
Customisation of the learning experience to achieve your goals -

Bespoke outlines
Practical hands-on exercises containing data / scenarios recognisable to the learners

Training scheduled on a date of your choice
Delivered online, onsite/classroom or hybrid by experts sharing real world experience

Private Group Prices RRP from €6840 online delivery, based on a group of 2 delegates, €2160 per additional delegate (excludes any certification / exam costs). We recommend a maximum group size of 12 for most learning events.