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 Maryland, 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 Maryland 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
MD, Baltimore - Legg Mason Tower
100 International Drive 23rd Floor, Baltimore, United States, 21202
A state-of-the-art, 24-story glass skyscraper that sits on the edge of Baltimore's Inner Harbor is the signature home of the Legg Mason office. It's located on the 23rd floor of this class-A office development, which is designed by world-renowned architects and boasts panoramic views. The office space benefits from the tower's ‘green' LEED credentials, proximity to Interstate 83 and excellent onsite amenities. Inner Harbor is the chief commercial and tourist destination in Baltimore and part of the Downtown area - the base for many key businesses.
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.
Chinese GPU architectures such as Huawei Ascend, Biren, and Cambricon MLUs offer CUDA alternatives tailored for local AI and HPC markets.
This instructor-led, live training (online or onsite) is aimed at advanced-level GPU programmers and infrastructure specialists who wish to migrate and optimize existing CUDA applications for deployment on Chinese hardware platforms.
By the end of this training, participants will be able to:
Evaluate compatibility of existing CUDA workloads with Chinese chip alternatives.
Port CUDA codebases to Huawei CANN, Biren SDK, and Cambricon BANGPy environments.
Compare performance and identify optimization points across platforms.
Address practical challenges in 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 platform or CUDA project, please contact us to arrange.
Cambricon MLUs (Machine Learning Units) are specialized AI chips optimized for inference and training in edge and datacenter scenarios.
This instructor-led, live training (online or onsite) is aimed at intermediate-level developers who wish to build and deploy AI models using the BANGPy framework and Neuware SDK on Cambricon MLU hardware.
By the end of this training, participants will be able to:
Set up and configure the BANGPy and Neuware development environments.
Develop and optimize Python- and C++-based models for Cambricon MLUs.
Deploy models to edge and data center devices running Neuware runtime.
Integrate ML workflows with MLU-specific acceleration features.
Format of the Course
Interactive lecture and discussion.
Hands-on use of BANGPy and Neuware for development and deployment.
Guided exercises focused on optimization, integration, and testing.
Course Customization Options
To request a customized training for this course based on your Cambricon device model or use case, please contact us to arrange.
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