Behind the high-performance capabilities of modern AI lies CANN (Compute Architecture for Neural Networks), the essential software layer that drives Huawei’s Ascend chips and the intelligent systems built upon them.
These instructor-led programs provide a deep dive into the Compute Architecture for Neural Networks, revealing how CANN connects algorithms with silicon through advanced techniques such as graph optimization, kernel fusion, and hardware-aware scheduling.
Whether you are developing inference engines, refining custom operators, or migrating deep learning models for edge deployment, you will acquire practical expertise in extracting optimal performance from Ascend processors.
Participate in live online sessions via an interactive remote desktop, or attend onsite workshops in Dobrogea Sud-Est—either at your company’s location or a NobleProg training center. These sessions include hands-on labs that simulate real-world acceleration and deployment workflows.
Often referred to as Ascend CANN or Huawei CANN, this training is designed to help developers, engineers, and AI infrastructure teams fully harness the power of hardware-aware intelligence.
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Huawei Ascend comprises a series of AI processors engineered for high-performance inference and training tasks.
This instructor-led live training, available online or in-person, targets intermediate AI engineers and data scientists looking to develop and optimize neural network models through Huawei’s Ascend platform and the CANN toolkit.
Upon completing this training, participants will be capable of:
Configuring and setting up the CANN development environment.
Building AI applications leveraging MindSpore and CloudMatrix workflows.
Enhancing performance on Ascend NPUs via custom operators and tiling techniques.
Deploying models to either cloud or edge environments.
Course Format
Interactive lectures accompanied by discussions.
Practical application of the Huawei Ascend platform and CANN toolkit within sample applications.
Guided exercises concentrating on model construction, training, and deployment.
Customization Options
For customized training tailored to your specific infrastructure or datasets, please reach out to us to make arrangements.
Huawei’s AI stack, spanning from the low-level CANN SDK to the high-level MindSpore framework, provides a tightly integrated environment for AI development and deployment, specifically optimized for Ascend hardware.
This instructor-led live training, available either online or onsite, targets technical professionals at beginner to intermediate levels who want to understand how CANN and MindSpore components collaborate to support AI lifecycle management and infrastructure decisions.
Upon completing this training, participants will be able to:
Comprehend the layered architecture of Huawei’s AI compute stack.
Identify how CANN facilitates model optimization and hardware-level deployment.
Assess the MindSpore framework and its toolchain in comparison to industry alternatives.
Contextualize Huawei's AI stack within enterprise or cloud/on-premises environments.
Course Format
Interactive lectures and discussions.
Live system demonstrations and case-based walkthroughs.
Optional guided labs exploring the model flow from MindSpore to CANN.
Course Customization Options
For customized training requests for this course, please contact us to arrange.
The CANN SDK (Compute Architecture for Neural Networks) offers robust deployment and optimization solutions for real-time AI applications in computer vision and NLP, particularly on Huawei Ascend hardware.
This instructor-led training, available online or onsite, targets intermediate-level AI practitioners looking to develop, deploy, and optimize vision and language models using the CANN SDK for production scenarios.
By the conclusion of this training, participants will be able to:
Deploy and optimize CV and NLP models utilizing CANN and AscendCL.
Leverage CANN tools to convert models and seamlessly integrate them into live pipelines.
Enhance inference performance for tasks such as detection, classification, and sentiment analysis.
Construct real-time CV/NLP pipelines tailored for edge or cloud deployment environments.
Course Format
Interactive lectures combined with live demonstrations.
Practical labs focusing on model deployment and performance profiling.
Live pipeline design exercises using real-world CV and NLP scenarios.
Course Customization Options
For customized training requests, please contact us to arrange details.
Leveraging CANN TIK (Tensor Instruction Kernel) alongside Apache TVM facilitates the sophisticated optimization and customization of AI model operators tailored for Huawei Ascend hardware.
This instructor-led, live training session, available in both online and onsite formats, is designed for advanced system developers looking to construct, deploy, and fine-tune custom operators for AI models. The curriculum focuses on utilizing CANN’s TIK programming model in conjunction with TVM compiler integration.
Upon completion of this training, participants will be equipped to:
Author and validate custom AI operators employing the TIK DSL specifically for Ascend processors.
Seamlessly integrate custom operators into the CANN runtime and execution graph.
Utilize TVM for operator scheduling, automatic tuning, and performance benchmarking.
Debug and enhance instruction-level performance to address custom computation patterns.
Course Format
Engaging lectures coupled with practical demonstrations.
Practical coding exercises involving operators via TIK and TVM pipelines.
Testing and optimization performed on Ascend hardware or simulated environments.
Course Customization Options
For bespoke training arrangements, please reach out to us to coordinate your specific requirements.
Huawei's Ascend CANN toolkit empowers developers to execute high-performance AI inference on edge devices like the Ascend 310. By providing essential tools for compiling, optimizing, and deploying models in environments with limited compute and memory resources, CANN addresses the unique challenges of edge computing.
This instructor-led live training, available online or onsite, is designed for intermediate-level AI developers and integrators looking to deploy and optimize models on Ascend edge devices using the CANN toolchain.
Upon completing this course, participants will be able to:
Prepare and convert AI models for the Ascend 310 using CANN tools.
Construct lightweight inference pipelines utilizing MindSpore Lite and AscendCL.
Enhance model performance within constrained compute and memory environments.
Deploy and monitor AI applications in real-world edge scenarios.
Course Format
Interactive lectures paired with live demonstrations.
Practical lab exercises featuring edge-specific models and use cases.
Live deployment examples on both virtual and physical edge hardware.
Customization Options
For tailored training options, please reach out to us to arrange your personalized session.
CANN (Compute Architecture for Neural Networks) is Huawei's specialized AI computing toolkit designed to compile, optimize, and deploy artificial intelligence models on Ascend AI processors.
This instructor-led live training session, available either online or on-site, targets beginner-level AI developers. The course explores how CANN integrates into the entire model lifecycle—from training through to deployment—and demonstrates its compatibility with popular frameworks such as MindSpore, TensorFlow, and PyTorch.
Upon completing this training, participants will be equipped to:
Comprehend the purpose and architectural design of the CANN toolkit.
Configure a development environment utilizing both CANN and MindSpore.
Convert and deploy a basic AI model onto Ascend hardware.
Acquire foundational insights necessary for future projects involving CANN optimization or integration.
Course Format
Engaging lectures paired with interactive discussions.
Practical, hands-on labs focused on simple model deployment.
Detailed, step-by-step guidance through the CANN toolchain and its integration points.
Customization Options
For tailored training sessions regarding this course, please reach out to us to arrange details.
The CANN SDK (Compute Architecture for Neural Networks) serves as Huawei’s foundational AI compute platform, enabling developers to refine and optimize the performance of neural networks deployed on Ascend AI processors.
This instructor-led live training (available online or onsite) targets advanced AI developers and system engineers seeking to boost inference performance through CANN’s sophisticated toolkit, which includes the Graph Engine, TIK, and custom operator development capabilities.
Upon completing this training, participants will be equipped to:
Comprehend CANN’s runtime architecture and its performance lifecycle.
Utilize profiling tools and the Graph Engine to analyze and enhance performance.
Develop and optimize custom operators leveraging TIK and TVM.
Address memory bottlenecks and elevate model throughput.
Course Format
Interactive lectures accompanied by group discussions.
Practical labs featuring real-time profiling and operator tuning.
Optimization exercises based on deployment scenarios with edge-case constraints.
Customization Options
For personalized training arrangements, please contact us.
CANN (Compute Architecture for Neural Networks) serves as Huawei’s AI computing stack, designed to facilitate the deployment and optimization of AI models on Ascend AI processors.
This instructor-led live training session, available either online or on-site, is designed for intermediate-level AI developers and engineers aiming to efficiently deploy trained AI models onto Huawei Ascend hardware. The curriculum utilizes the CANN toolkit along with tools like MindSpore, TensorFlow, and PyTorch.
Upon completion of this training, participants will be able to:
Gain a comprehensive understanding of the CANN architecture and its critical role within the AI deployment pipeline.
Convert and adapt models from leading frameworks into formats compatible with Ascend processors.
Utilize tools such as ATC, OM model conversion utilities, and MindSpore for both edge and cloud inference tasks.
Troubleshoot deployment challenges and optimize performance on Ascend hardware.
Course Format
Interactive lectures combined with live demonstrations.
Practical lab exercises utilizing CANN tools and Ascend simulators or physical devices.
Real-world AI model deployment scenarios for hands-on experience.
Course Customization Options
For tailored training needs related to this course, please reach out to us to make arrangements.
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