Cambricon (MLU) Training in Columbia

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:

• Evaluate models on Ascend, Biren, and Cambricon platforms.
• Identify system bottlenecks and memory/compute inefficiencies.
• Implement graph-level, kernel-level, and operator-level optimizations.
• Adjust deployment pipelines to enhance throughput and reduce latency.

Format of the Course

• Interactive lecture and discussion.
• Practical use of profiling and optimization tools on each platform.
• Guided exercises focused on real-world tuning scenarios.

Course Customization Options

• To request a customized training for government based on your specific performance environment or model type, please contact us to arrange.

Chinese GPU architectures, including Huawei Ascend, Biren, and Cambricon MLUs, offer CUDA alternatives specifically designed for the 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 for government use.

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

• Evaluate the compatibility of existing CUDA workloads with Chinese chip alternatives.
• Port CUDA codebases to Huawei CANN, Biren SDK, and Cambricon BANGPy environments.
• Compare performance metrics and identify optimization opportunities across different platforms.
• Address practical challenges in supporting and deploying applications across multiple architectures.

Format of the Course

• Interactive lecture and discussion sessions.
• 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 specific platform or CUDA project, please contact us to arrange.

Cambricon MLUs (Machine Learning Units) are specialized AI chips designed to optimize inference and training in edge and data center environments.

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 for government applications.

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

• Set up and configure the BANGPy and Neuware development environments for government use.
• Develop and optimize Python- and C++-based models for Cambricon MLUs in alignment with public sector workflows.
• Deploy models to edge and data center devices running Neuware runtime, ensuring compliance with governance standards.
• Integrate machine learning workflows with MLU-specific acceleration features to enhance performance and accountability.

Format of the Course

• Interactive lecture and discussion focused on government applications.
• Hands-on use of BANGPy and Neuware for development and deployment in a public sector context.
• Guided exercises centered on optimization, integration, and testing tailored to government needs.

Course Customization Options

• To request a customized training for this course based on your Cambricon device model or specific use case for government, please contact us to arrange.