Online Cambricon (MLU) Training in the UAE

The leading AI hardware platforms in China—Ascend, Biren, and Cambricon—provide distinctive acceleration and profiling tools tailored for large-scale AI workloads.

This instructor-led training session (conducted either online or on-site) is designed for advanced-level engineers specializing in AI infrastructure and performance. The goal is to enhance their ability to optimize model inference and training processes across various Chinese AI chip platforms.

Upon completion of this course, participants will be able to:

• Evaluate models using the Ascend, Biren, and Cambricon platforms.
• Detect system limitations and inefficiencies in memory and compute resources.
• Implement optimizations at the graph level, kernel level, and operator level.
• Refine deployment pipelines to boost throughput and reduce latency.

Course Format

• An interactive lecture combined with discussions.
• Practical use of profiling and optimization tools on each platform.
• Guided exercises centered around real-world tuning scenarios.

Customization Options for the Course

• If you require a customized training session based on your specific performance environment or model type, please contact us to arrange this.

Chinese GPU architectures like Huawei Ascend, Biren, and Cambricon MLUs provide alternatives to CUDA that are specifically designed for the local AI and HPC markets in the UAE.

This instructor-led training (online or at your location) is targeted at advanced GPU programmers and infrastructure specialists who want to migrate and optimize existing CUDA applications for deployment on Chinese hardware platforms.

By the end of this course, participants will be able to:

• Determine the compatibility of current CUDA workloads with Chinese chip alternatives.
• Migrate CUDA codebases to environments such as Huawei CANN, Biren SDK, and Cambricon BANGPy.
• Analyze performance differences and pinpoint optimization opportunities across various platforms.
• Overcome practical challenges in cross-architecture support and deployment.

Course Format

• Interactive lectures and discussions.
• Hands-on labs for code translation and performance comparison.
• Guided exercises focusing on multi-GPU adaptation strategies.

Customization Options

• To request a customized training based on your platform or CUDA project, please contact us to arrange the details.

Cambricon MLUs (Machine Learning Units) are specialized AI chips designed to enhance performance in both inference and training tasks within edge computing and data center environments.

This instructor-led live training session (conducted either online or at your location) is tailored for intermediate developers looking to create and deploy AI models using the BANGPy framework alongside Neuware SDK on Cambricon MLU hardware.

Upon completion of this course, participants will be able to:

• Establish and configure development environments for BANGPy and Neuware.
• Create and refine Python- and C++-based models specifically for Cambricon MLUs.
• Deploy these models onto edge devices and data centers that utilize Neuware runtime.
• Incorporate ML workflows with features optimized for MLU acceleration.

Course Format

• Engaging lectures combined with interactive discussions.
• Practical hands-on experience using BANGPy and Neuware for both development and deployment tasks.
• Guided exercises centered on optimization, integration, and testing processes.

Customization Options

• If you require a customized training session based on your specific Cambricon device model or use case, please reach out to us for further arrangements.