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.
Enhance AI workload performance on Ascend, Biren, and Cambricon through this practical training in Plovdiv. Master model benchmarking, pinpoint bottlenecks, and deploy graph, kernel, and operator-level optimizations. Refine deployment pipelines to optimize throughput and latency across these top-tier platforms.
Transition CUDA applications to Chinese GPU ecosystems, such as Huawei Ascend and Biren, within Plovdiv. This instructor-led course assists advanced programmers in code translation and performance refinement, featuring practical labs for porting CUDA codebases to emerging SDKs.
This live, instructor-led training in Plovdiv provides developers with the competencies needed to construct and deploy AI models utilizing BANGPy and Neuware on Cambricon MLUs. Learners will manage environment configuration, build optimized models, and integrate MLU acceleration into both edge and data center applications.
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