Cambricon’s MLU chips go beyond being mere processors—they serve as China’s strategic response for scalable and efficient AI acceleration in cloud, edge, and data center environments.
This instructor-led training leads engineers and AI developers through the Cambricon ecosystem, covering everything from deep learning model deployment to performance optimization on MLU hardware.
Training sessions are offered as online live courses via interactive remote desktop or in person at Dobrogea Sud-Est, where practical labs reflect the AI challenges Cambricon technology is designed to address.
Whether you are expanding an AI lab or preparing a data center team for future needs, on-site sessions can be hosted at your premises in Dobrogea Sud-Est or at a NobleProg training center built for immersive technical education.
Also known as Cambricon AI, the MLU accelerator, or Machine Learning Unit, this training empowers teams to develop AI infrastructure that moves beyond traditional GPU-based approaches.
NobleProg – Your Local Training Provider
Constanta, Mircea cel Batran Street
NobleProg Constanta, Str. Mircea cel Batran, Constanta, romania, 900658
The building is situated in the central part of the city with view to the Black Sea. The hotel contains three conference rooms fully equipped for training, meetings and conferences. Just a quick walk and you can relax on the beach after a full day in the conference center.
Ascend, Biren, and Cambricon represent leading AI hardware platforms in China, each providing distinct acceleration and profiling tools tailored for large-scale AI workloads.
This instructor-led live training, available online or onsite, targets advanced AI infrastructure and performance engineers aiming to optimize model inference and training processes across these diverse Chinese AI chip ecosystems.
Upon completion of this training, participants will be equipped to:
Evaluate model performance on Ascend, Biren, and Cambricon platforms.
Pinpoint system bottlenecks and inefficiencies in memory and compute resources.
Implement optimizations at the graph, kernel, and operator levels.
Refine deployment pipelines to enhance throughput and reduce latency.
Course Format
Interactive lectures and discussions.
Practical application of profiling and optimization tools across each platform.
Guided exercises centered on real-world tuning scenarios.
Customization Options
To arrange customized training tailored to your specific performance environment or model type, please contact us.
Chinese GPU architectures, including Huawei Ascend, Biren, and Cambricon MLUs, provide CUDA alternatives specifically designed for the local AI and HPC markets.
This instructor-led, live training (available online or onsite) targets advanced-level GPU programmers and infrastructure specialists who aim to migrate and optimize existing CUDA applications for deployment on Chinese hardware platforms.
By the conclusion of this training, participants will be able to:
Evaluate the compatibility of existing CUDA workloads with Chinese chip alternatives.
Port CUDA codebases to Huawei CANN, Biren SDK, and Cambricon BANGPy environments.
Compare performance metrics and identify optimization opportunities across various platforms.
Address practical challenges related to cross-architecture support and deployment.
Format of the Course
Interactive lecture and discussion.
Hands-on code translation and performance comparison labs.
Guided exercises focused on multi-GPU adaptation strategies.
Course Customization Options
To request a customized training for this course based on your specific platform or CUDA project, please contact us to arrange.
Cambricon MLUs (Machine Learning Units) are specialized AI chips designed for optimized inference and training in both edge and data center environments.
This instructor-led live training (available online or onsite) is designed for intermediate-level developers who want to create and deploy AI models using the BANGPy framework and Neuware SDK on Cambricon MLU hardware.
Upon completing this training, participants will be able to:
Set up and configure development environments for BANGPy and Neuware.
Develop and optimize Python and C++ models for Cambricon MLUs.
Deploy models to edge and data center devices utilizing the Neuware runtime.
Integrate ML workflows with acceleration features specific to MLU.
Course Format
Interactive lectures and discussions.
Practical application of BANGPy and Neuware for development and deployment.
Guided exercises focusing on optimization, integration, and testing.
Customization Options
To request a customized training session tailored to your specific Cambricon device model or use case, please contact us to arrange.
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