As global GPU supply chains undergo transformation, innovation rises to meet the challenge — with Biren stepping forward as China’s decisive answer to high-performance AI computing.
Through these instructor-led courses, participants engage directly with Biren’s architecture: gaining expertise in memory throughput, optimizing parallel processing, and tuning deep learning models on BR100-class accelerators.
Training is offered via online live sessions through an interactive remote desktop environment, or as onsite live training in Sofia, incorporating exercises modeled after large-scale, real-world AI workloads.
Organizations located in Sofia may opt for onsite sessions at their own facilities or enroll in team-based courses at a NobleProg training center in Sofia.
Also known as the Biren GPU, BR100, or Chinese AI accelerator, this course track is vital for teams positioning themselves for the future of computational autonomy.
NobleProg – Your Local Training Provider
Crystal Business Center
ул. "Осогово" 40, Sofia, Bulgaria, 1303
Crystal Business Center is located in the central part of Sofia, on the corner of "Osogovo" street. and "Todor Aleksandrov" blvd. The building is easily accessible by metro (only 50 m from Opalchenska station) and other public transport. Its total area is 8000 sq.m. The office area is 6171 sq.m.
Ascend, Biren, and Cambricon stand as premier AI hardware platforms in China, providing distinct acceleration and profiling solutions tailored for large-scale AI workloads in production.
This instructor-led live training (available online or onsite) targets advanced AI infrastructure and performance engineers who aim to optimize model inference and training processes across various Chinese AI chip ecosystems.
Upon completion of this training, participants will be equipped to:
Evaluate models on Ascend, Biren, and Cambricon platforms through benchmarking.
Diagnose system bottlenecks and identify inefficiencies in memory and compute resources.
Implement optimizations at the graph, kernel, and operator levels.
Refine deployment pipelines to enhance both throughput and reduce latency.
Course Format
Interactive lectures and discussions.
Practical application of profiling and optimization tools specific to each platform.
Guided exercises designed around real-world tuning scenarios.
Customization Options
For customized training tailored to your specific performance environment or model requirements, please contact us to arrange.
Chinese GPU architectures, including Huawei Ascend, Biren, and Cambricon MLUs, provide CUDA alternatives specifically designed for the local AI and HPC markets.
This instructor-led, live training session (available online or onsite) targets advanced-level GPU programmers and infrastructure specialists seeking to migrate and optimize existing CUDA applications for deployment on Chinese hardware platforms.
Upon completion of this training, participants will be equipped 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 related to cross-architecture support and deployment.
Course Format
Interactive lectures and discussions.
Hands-on labs involving code translation and performance comparison.
Guided exercises focusing on multi-GPU adaptation strategies.
Customization Options
To request a customized training session tailored to your specific platform or CUDA project, please contact us to arrange it.
Biren AI Accelerators are high-performance GPUs designed for AI and HPC workloads with support for large-scale training and inference.
This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level developers who wish to program and optimize applications using Biren’s proprietary GPU stack, with practical comparisons to CUDA-based environments.
By the end of this training, participants will be able to:
Understand Biren GPU architecture and memory hierarchy.
Set up the development environment and use Biren’s programming model.
Translate and optimize CUDA-style code for Biren platforms.
Apply performance tuning and debugging techniques.
Format of the Course
Interactive lecture and discussion.
Hands-on use of Biren SDK in sample GPU workloads.
Guided exercises focused on porting and performance tuning.
Course Customization Options
To request a customized training for this course based on your application stack or integration needs, please contact us to arrange.
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