Cambricon’s MLU chips are not merely processors; they represent China’s strategic approach to scalable and efficient AI acceleration across cloud, edge, and data center settings.
Through this instructor-led training, engineers and AI developers navigate the Cambricon stack, covering the full spectrum from deploying deep learning models to optimizing performance on MLU hardware.
Courses are available as live online training conducted over interactive remote desktop, or as onsite sessions in Plovdiv, where practical labs replicate the real-world AI challenges that Cambricon technology is engineered to address.
Whether you are expanding an AI lab or preparing a data center team for the future, onsite sessions can be hosted at your facility in Plovdiv or at a NobleProg training center built for immersive technical learning.
Also known as Cambricon AI, MLU accelerator, or Machine Learning Unit, this training supports teams developing AI infrastructure beyond the standard GPU approach.
NobleProg – Your Local Training Provider
Business Center Plovdiv
Han Kubrat St 1, Plovdiv, Bulgaria, 4017
This is the most modern business center in the city, with all the necessary functionalities, while being located in a green part of the city.
It is about 20 minutes by bus from the main train station as well as the city center.
Optimize AI workloads on Ascend, Biren, and Cambricon with this hands-on training in Plovdiv. Learn to benchmark models, identify bottlenecks, and apply graph, kernel, and operator-level optimizations. Tune deployment pipelines to enhance throughput and latency across these leading platforms.
Migrate CUDA applications to Chinese GPU architectures like Huawei Ascend and Biren in Plovdiv. This instructor-led course guides advanced programmers through code translation and performance optimization, covering hands-on labs for porting CUDA codebases to new SDKs.
This instructor-led live training in Plovdiv equips developers with the skills to build and deploy AI models using BANGPy and Neuware on Cambricon MLUs. Participants will configure environments, develop optimized models, and integrate MLU acceleration into edge and data center applications.
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