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 Lahore, 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 Lahore 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
Lahore - Classroom
The Enterprise, Multan Road, Lahore, pakistan, 54500
For Sales Enquires and Meetings
Please note that, in most cases, usually we are not able to organise ad hoc sales meetings, especially on our classrooms as they are all occupied with ongoing training sessions . Please contact us by e-mail or phone at least one day earlier to make an appointment with one of our consultants at our corporate office
Optimize AI workloads on Ascend, Biren, and Cambricon with this hands-on training in Lahore. 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 Lahore. 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 Lahore 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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