Performance Optimization on Ascend, Biren, and Cambricon Training Course

Huawei Ascend comprises a series of artificial intelligence processors engineered to deliver high-performance capabilities for both inference and training workloads.

This instructor-led, live training session, available via online or onsite formats, targets intermediate-level AI engineers and data scientists seeking to build and optimize neural network models utilizing the Huawei Ascend platform alongside the CANN toolkit.

Upon completion of this program, participants will be equipped to:

• Establish and configure the CANN development environment.
• Create AI applications utilizing MindSpore and CloudMatrix workflows.
• Enhance performance on Ascend NPUs through the implementation of custom operators and tiling techniques.
• Deploy models within edge or cloud infrastructure.

Course Delivery Format

• Engaging lectures and discussions.
• Practical application of Huawei Ascend hardware and the CANN toolkit within sample applications.
• Supervised exercises centered on model construction, training, and deployment processes.

Course Customization Options

• Agencies seeking tailored training aligned with their specific infrastructure or datasets may contact the provider to arrange these services for government use cases.

The Compute Architecture for Neural Networks (CANN) serves as Huawei’s comprehensive AI compute framework designed to facilitate the deployment and optimization of artificial intelligence models on Ascend processors.

This instructor-led session, available via online or onsite delivery, is designed for intermediate-level developers and engineers seeking to efficiently implement trained AI models on Huawei Ascend hardware utilizing the CANN toolkit alongside frameworks such as MindSpore, TensorFlow, or PyTorch. The curriculum emphasizes scalable solutions tailored for government and enterprise environments for government operations.

Upon completion of this training, participants will demonstrate proficiency in:

• Analyzing the CANN architecture and its function within the AI deployment lifecycle.
• Converting and adapting models from widely used frameworks into formats compatible with Ascend processors.
• Utilizing utilities such as ATC, OM model conversion, and MindSpore to support inference tasks across edge and cloud infrastructure.
• Identifying deployment challenges and executing performance optimizations on Ascend hardware platforms.

Course Delivery Structure

• Interactive instruction combined with technical demonstrations.
• Practical laboratory exercises utilizing CANN tools within Ascend simulator or hardware environments.
• Application of deployment strategies based on real-world AI use cases.

Training Adaptation Options

• For agencies requiring a tailored curriculum, please contact our administrative team to coordinate custom arrangements.

Huawei’s CloudMatrix serves as a consolidated platform for the development and deployment of artificial intelligence solutions, engineered to facilitate scalable, enterprise-ready inference workflows.

This instructor-led program, available in online or onsite formats, targets beginner to intermediate-level practitioners seeking to deploy and monitor AI models via the CloudMatrix environment, utilizing CANN and MindSpore integration capabilities for government and public sector applications.

Upon completion of this curriculum, learners will be equipped to:

• Utilize CloudMatrix to package, deploy, and serve models effectively.
• Convert and optimize workloads for Ascend hardware architectures.
• Configure pipelines to support both real-time and batch inference operations.
• Oversee deployments and optimize performance within production environments.

Training Methodology

• Engaging lectures coupled with guided discussions.
• Practical application of CloudMatrix through real-world deployment scenarios.
• Structured exercises emphasizing model conversion, optimization, and scalability.

Customization Availability

• Organizations requiring tailored training aligned with their specific AI infrastructure or cloud environments should contact the administration to schedule.

Biren AI Accelerators are high-performance graphics processing units engineered for artificial intelligence and high-performance computing tasks, providing robust support for extensive model training and inference operations.

This instructor-led training, available in online or onsite formats, targets developers ranging from intermediate to advanced proficiency who seek to program and optimize applications using Biren’s proprietary GPU software stack, with practical comparisons to CUDA-based environments.

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

• Comprehend the architecture and memory hierarchy of Biren GPUs.
• Configure development environments and utilize Biren’s programming interface.
• Adapt and optimize CUDA-style code for Biren hardware platforms.
• Implement performance tuning and debugging methodologies.

Course Format

• Interactive lectures and discussions.
• Practical application of the Biren SDK within sample GPU workloads.
• Directed exercises emphasizing code porting and performance optimization.

Course Customization Options

• Agencies requiring tailored training for specific application stacks or integration requirements should contact the provider to arrange a customized session for government personnel.

Cambricon Machine Learning Units (MLUs) represent specialized artificial intelligence processors engineered to support inference and training workloads across both edge computing and data center environments.

This instructor-led live training, available either online or onsite, targets intermediate-level software developers seeking to construct and deploy artificial intelligence models utilizing the BANGPy framework and Neuware SDK within Cambricon MLU hardware architectures. The program is designed for government applications where efficient AI deployment is critical.

Upon completion of this instructional session, participants will be capable of:

• Establishing and configuring development environments for both BANGPy and Neuware.
• Creating and refining Python- and C++-based models optimized for Cambricon MLU performance.
• Deploying trained models to edge and data center systems operating on the Neuware runtime.
• Integrating machine learning workflows with acceleration features specific to MLU technology.

Course Format

• Interactive lectures paired with technical discussions.
• Practical application of BANGPy and Neuware for development and deployment tasks.
• Structured exercises focusing on optimization, system integration, and validation.

Customization Options

• Agencies may request tailored training sessions aligned with their specific Cambricon device models or operational use cases by contacting the training provider to arrange details.

CANN (Compute Architecture for Neural Networks) is Huawei’s AI computing toolkit used to compile, optimize, and deploy AI models on Ascend AI processors, offering critical resources for government workloads that require scalable intelligence solutions.

This instructor-led, live training (online or onsite) is aimed at beginner-level AI developers who wish to understand how CANN fits into the model lifecycle from training to deployment, and how it works with frameworks like MindSpore, TensorFlow, and PyTorch.

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

• Understand the purpose and architecture of the CANN toolkit.
• Set up a development environment with CANN and MindSpore.
• Convert and deploy a simple AI model to Ascend hardware.
• Gain foundational knowledge for future CANN optimization or integration projects.

Format of the Course

• Interactive lecture and discussion.
• Hands-on labs with simple model deployment.
• Step-by-step walkthrough of the CANN toolchain and integration points.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Huawei's Ascend CANN toolkit facilitates advanced artificial intelligence inference on edge hardware, including the Ascend 310 platform. This framework supplies critical capabilities for compiling, optimizing, and deploying models in environments with restricted computational resources and memory capacity, ensuring effective solutions for government systems that require efficient edge processing.

This instructor-led live training program, available online or onsite, targets intermediate-level AI developers and integrators seeking to deploy and optimize machine learning models on Ascend edge devices via the CANN toolchain.

Upon completion of this course, participants will be capable of:

• Preparing and converting artificial intelligence models for the Ascend 310 platform utilizing CANN utilities.
• Developing lightweight inference pipelines through MindSpore Lite and AscendCL integration.
• Enhancing model performance within resource-constrained compute and memory contexts.
• Deploying and monitoring artificial intelligence applications in practical edge scenarios.

Course Format

• Interactive lectures combined with live demonstrations.
• Practical laboratory exercises focused on edge-specific models and operational scenarios.
• Real-time deployment examples executed on virtual or physical edge infrastructure.

Course Customization Options

• To request customized training for this course, please contact us to arrange a suitable schedule.

Huawei provides a cohesive AI infrastructure that spans from the foundational CANN software development kit to the advanced MindSpore framework. This environment is specifically engineered to facilitate efficient AI model development and deployment on Ascend-based computing hardware.

This guided instructional session, available via live online or in-person formats, targets technical practitioners at beginner and intermediate proficiency levels. The curriculum focuses on elucidating the synergistic relationship between CANN and MindSpore components to inform strategic AI lifecycle management and infrastructure planning. Designed for government professionals, the training supports informed decision-making regarding public sector technology adoption.

Upon completion of this instructional module, participants will be equipped to:

• Analyze the hierarchical structure of Huawei’s AI computing architecture.
• Assess the role of CANN in optimizing model performance and executing hardware-level deployment.
• Evaluate the MindSpore framework and associated toolchain against comparable industry solutions.
• Determine the appropriate placement of Huawei’s AI stack within enterprise, cloud-based, or on-premises operational environments.

Course Delivery Format

• Structured lectures coupled with interactive group discussions.
• Real-time system demonstrations and analysis of practical case studies.
• Optional supervised laboratory exercises focusing on the workflow from MindSpore to CANN.

Customization Availability

• Organizations seeking tailored training for this course are encouraged to contact our team to arrange specific requirements.

The Compute Architecture for Neural Networks (CANN) SDK serves as Huawei’s foundational platform for AI computing. It enables engineers to refine and enhance the operational efficiency of neural networks deployed on Ascend AI processors.

This instructor-led training program, available through online or onsite delivery methods, is designed for advanced artificial intelligence developers and system engineers seeking to maximize inference capabilities. Participants will utilize CANN’s comprehensive toolset, specifically focusing on the Graph Engine, TIK, and custom operator development frameworks.

Upon completion of this educational module, participants will demonstrate proficiency in:

• Analyzing CANN's runtime architecture and performance lifecycle.
• Leveraging profiling tools and the Graph Engine for detailed performance assessment and optimization.
• Developing and refining custom operators using TIK and TVM technologies.
• Addressing memory constraints and enhancing model throughput efficiency.

Course Delivery Format

• Structured interactive lectures and technical discussions.
• Practical laboratory exercises featuring real-time profiling and operator tuning.
• Targeted optimization workshops utilizing edge-case deployment scenarios.

Customization Options for Government

• To request a customized training curriculum tailored to specific organizational needs, please contact our support team to make arrangements.

The CANN SDK (Compute Architecture for Neural Networks) delivers robust deployment and optimization capabilities for real-time artificial intelligence workloads in computer vision and natural language processing, particularly when utilized with Huawei Ascend hardware.

This instructor-led training program, available via online or onsite delivery, is designed for intermediate-level AI professionals seeking to develop, deploy, and optimize vision and language models using the CANN SDK for government and production-grade applications.

Upon completion of this training, participants will be equipped to:

• Deploy and optimize computer vision (CV) and natural language processing (NLP) models utilizing CANN and AscendCL.
• Leverage CANN utilities to convert models and integrate them into operational pipelines.
• Enhance inference performance for critical tasks including detection, classification, and sentiment analysis.
• Construct real-time CV/NLP pipelines tailored for edge or cloud-based deployment architectures.

Course Format

• Interactive lectures coupled with live demonstrations.
• Practical laboratory exercises focused on model deployment and performance profiling.
• Live pipeline development using authentic CV and NLP use cases.

Course Customization Options

• Agencies requiring customized training for government-specific needs should contact us to arrange tailored sessions.

CANN TIK (Tensor Instruction Kernel) and Apache TVM facilitate sophisticated optimization and customization of AI model operators for Huawei Ascend hardware.

This instructor-led, live training (available online or onsite) targets advanced system developers seeking to build, deploy, and tune custom operators for AI models via CANN’s TIK programming model and TVM compiler integration. Designed specifically for government applications, this course supports critical computational needs.

Upon completion of this training, participants will be capable of:

• Writing and validating custom AI operators using the TIK DSL for Ascend processors.
• Integrating custom operations into the CANN runtime and execution graph infrastructure.
• Leveraging TVM for operator scheduling, auto-tuning, and benchmarking procedures.
• Debugging and optimizing instruction-level performance for specialized computation patterns.

Course Format

• Interactive lectures combined with technical demonstrations.
• Practical coding exercises involving operators through TIK and TVM pipelines.
• Testing and tuning activities performed on Ascend hardware or simulation environments.

Customization Options

• To request tailored training for this curriculum, please contact the program coordinators to make arrangements.

Domestic graphical processing unit (GPU) designs, including those from Huawei Ascend, Biren, and Cambricon, provide viable solutions for CUDA-based workloads within regional artificial intelligence and high-performance computing sectors. This facilitated instruction, available via digital or physical venues, targets experienced GPU developers and infrastructure analysts seeking to transition and enhance current CUDA implementations for integration with Chinese silicon platforms.

Upon completion of this program, learners will be equipped to:

• Assess the interoperability of legacy CUDA tasks with domestic semiconductor options.
• Translate CUDA source code into Huawei CANN, Biren SDK, and Cambricon BANGPy environments.
• Analyze performance metrics and locate optimization opportunities across diverse architectures.

Navigate operational hurdles associated with cross-platform compatibility and system deployment.

Course Structure

• Seminar-based instruction coupled with interactive dialogue.
• Practical laboratory sessions involving code migration and benchmarking.
• Instructor-supervised activities concentrating on multi-GPU adaptation techniques.

Training Modifications

• To arrange a customized learning experience aligned with your specific platform or CUDA initiative, please contact our office. This curriculum is designed for government use and other professional audiences seeking robust computational strategies for government environments.