Cambricon’s MLU chips go beyond being mere processors; they represent China’s approach to scalable and efficient AI acceleration within cloud, edge, and data center environments.
This instructor-led training leads engineers and AI developers through the Cambricon ecosystem, covering everything from deploying deep learning models to optimizing performance on MLU hardware.
Courses are conducted either as online live training via interactive remote desktop or on-site in Lyon, where hands-on labs reflect the AI challenges that Cambricon technology is designed to address.
Whether you are expanding an AI lab or preparing a data center team for the future, on-site sessions can be held at your facility in Lyon or at a NobleProg training center built for immersive technical learning.
Also known as Cambricon AI, MLU accelerator, or Machine Learning Unit, this training assists teams in developing AI infrastructure that extends beyond traditional GPU solutions.
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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