Cambricon’s MLU chips aren’t just processors — they’re China’s answer to scalable, efficient AI acceleration across cloud, edge, and data center environments.
This instructor-led training guides engineers and AI developers through the Cambricon stack: from deep learning model deployment to performance optimization on MLU hardware.
Courses are delivered either as online live training via interactive remote desktop, or onsite in Lyon, where hands-on labs mirror the AI challenges Cambricon is built to solve.
Whether you're scaling up an AI lab or future-proofing a data center team, onsite sessions can take place at your facility in Lyon or in a NobleProg training center designed for immersive technical learning.
Also referred to as Cambricon AI, MLU accelerator, or Machine Learning Unit, this training supports teams building AI infrastructure beyond the conventional GPU path.
NobleProg – Your Local Training Provider
Lyon, Swisslife Tower
NobleProg Lyon, 10 Place Charles Béraudier, Lyon, france, 69000
Located 200 meters far from the train station TGV, Swisslife Tower is today the most representative building of this quarter of Lyon. The Business Center offers you a perfect location for your training.
Gares TGV
100meters from Gare TGV Part-Dieu , porte du Rhône Exit
Aéroport
30 minutes from Lyon Saint Exupéry (Satolas)
Rhône Express from Saint Exupéry airport (Terminus Gare part-Dieu)
Ascend, Biren, and Cambricon represent the leading AI hardware platforms in China, each providing distinct acceleration and profiling capabilities for enterprise-scale AI workloads.
This instructor-led live training, available online or onsite, is designed for advanced AI infrastructure and performance engineers seeking to optimize model inference and training workflows across these diverse Chinese AI chip ecosystems.
Upon completion of this training, participants will be equipped to:
Benchmark models across Ascend, Biren, and Cambricon platforms.
Identify system bottlenecks and inefficiencies in memory and compute resources.
Implement optimizations at the graph, kernel, and operator levels.
Tune deployment pipelines to enhance throughput and reduce latency.
Course Format
Interactive lectures and discussions.
Practical application of profiling and optimization tools on each respective platform.
Guided exercises centered on real-world tuning scenarios.
Customization Options
To request a customized version of this course tailored to your specific performance environment or model architecture, please contact us to arrange.
Chinese GPU architectures, including Huawei Ascend, Biren, and Cambricon MLUs, provide alternatives to CUDA specifically designed for the domestic AI and high-performance computing (HPC) markets.
This instructor-led live training, available either online or onsite, targets advanced GPU developers and infrastructure specialists looking to migrate and optimize existing CUDA applications for deployment on Chinese hardware platforms.
Upon completion of this training, participants will be able to:
Assess the compatibility of current CUDA workloads with Chinese chip alternatives.
Port CUDA codebases to Huawei CANN, Biren SDK, and Cambricon BANGPy environments.
Compare performance metrics and identify key optimization opportunities across different platforms.
Address practical challenges related to cross-architecture support and deployment.
Format of the Course also allows for the evaluation of participants.
Interactive lectures and discussions.
Hands-on labs for code translation and performance comparison.
Guided exercises focusing on multi-GPU adaptation strategies.
Course Customization Options
To request customized training tailored to your specific platform or CUDA project, please contact us to arrange it.
Cambricon MLUs (Machine Learning Units) are specialized AI processors designed to optimize both inference and training workloads for edge computing and data center environments.
This instructor-led live training (available online or onsite) targets intermediate developers looking to build and deploy AI models utilizing the BANGPy framework and the Neuware SDK on Cambricon MLU hardware.
Upon completion of this course, participants will be able to:
Set up and configure development environments for BANGPy and Neuware.
Develop and optimize models written in Python and C++ for Cambricon MLUs.
Deploy models to edge and data center devices running the Neuware runtime.
Integrate machine learning workflows with MLU-specific acceleration capabilities.
Course Format
Interactive lectures and discussions.
Practical, hands-on experience with BANGPy and Neuware for development and deployment.
Guided exercises focusing on optimization, integration, and testing.
Customization Options
To arrange a customized version of this course tailored to your specific Cambricon device model or use case, please contact us.
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