Cambricon MLU Development with BANGPy and Neuware Training Course
Cambricon MLUs (Machine Learning Units) are specialized AI chips designed to excel at inference and training tasks in both edge and datacenter environments.
This instructor-led, live course, available online or onsite, is tailored for intermediate developers seeking to build and deploy AI models leveraging the BANGPy framework and Neuware SDK on Cambricon MLU hardware.
Upon completion, participants will have the ability to:
- Establish and configure BANGPy and Neuware development environments.
- Build and optimize models in Python and C++ specifically for Cambricon MLUs.
- Deploy models to edge and data center devices operating the Neuware runtime.
- Incorporate ML workflows with MLU-specific acceleration capabilities.
Course Delivery Style
- Engaging lectures combined with open discussions.
- Practical application of BANGPy and Neuware in development and deployment tasks.
- Structured exercises emphasizing optimization, integration, and testing.
Tailored Learning Options
- For a customized training experience aligned with your specific Cambricon device model or use case, please reach out to us to make arrangements.
Course Outline
Fundamentals of Cambricon and MLU Architecture
- Overview of Cambricon’s AI chip portfolio
- MLU architecture and instruction pipelines
- Supported model types and applicable use cases
Setting Up the Development Toolchain
- Installation of BANGPy and the Neuware SDK
- Configuring environments for Python and C++
- Model compatibility checks and preprocessing
Building Models with BANGPy
- Managing tensor structures and shapes
- Constructing computation graphs
- Implementing custom operations in BANGPy
Deployment via Neuware Runtime
- Converting and loading models
- Controlling execution and inference
- Best practices for edge and data center deployment
Optimizing Performance
- Tuning memory mapping and layers
- Tracing execution and profiling performance
- Identifying common bottlenecks and implementing solutions
Integrating MLUs into Applications
- Leveraging Neuware APIs for application integration
- Supporting streaming and multi-model scenarios
- Handling hybrid CPU-MLU inference workflows
End-to-End Project and Practical Application
- Lab session: Deploying a vision or NLP model
- Edge inference implementation with BANGPy integration
- Evaluating accuracy and throughput
Conclusion and Recommended Next Steps
Requirements
- A solid grasp of machine learning model architectures
- Proficiency in Python and/or C++
- Knowledge of model deployment and acceleration principles
Target Audience
- Embedded AI developers
- ML engineers focused on edge or datacenter deployments
- Developers specializing in Chinese AI infrastructure
Need help picking the right course?
southafrica@nobleprog.co.za or +27 (0)10 005 5793
Cambricon MLU Development with BANGPy and Neuware Training Course - Enquiry
Testimonials (1)
That we can cover advance topic and work with real-life example
Ruben Khachaturyan - iris-GmbH infrared & intelligent sensors
Course - Advanced Edge AI Techniques
Related Courses
5G and Edge AI: Enabling Ultra-Low Latency Applications
21 HoursThis instructor-led, live training in Botswana (online or onsite) is designed for intermediate-level telecom professionals, AI engineers, and IoT specialists who wish to investigate how 5G networks accelerate Edge AI applications.
By the end of this training, participants will be able to:
- Grasp the fundamentals of 5G technology and its impact on Edge AI.
- Deploy AI models optimised for low-latency applications within 5G environments.
- Implement real-time decision-making systems using Edge AI and 5G connectivity.
- Optimise AI workloads for efficient performance on edge devices.
6G and the Intelligent Edge
21 HoursThis 21-hour training in Botswana empowers IT architects to design next-generation distributed systems. By exploring 6G, edge computing, and AI integration, you will learn to build low-latency, scalable infrastructures. Develop practical skills to create secure, resilient, and intelligent edge architectures that meet future business demands.
Advanced Edge AI Techniques
14 HoursThis instructor-led, live training in Botswana (online or onsite) is aimed at advanced-level AI practitioners, researchers, and developers who wish to master the latest advancements in Edge AI, optimize their AI models for edge deployment, and explore specialized applications across various industries.
By the end of this training, participants will be able to:
- Explore advanced techniques in Edge AI model development and optimization.
- Implement cutting-edge strategies for deploying AI models on edge devices.
- Utilize specialized tools and frameworks for advanced Edge AI applications.
- Optimize performance and efficiency of Edge AI solutions.
- Explore innovative use cases and emerging trends in Edge AI.
- Address advanced ethical and security considerations in Edge AI deployments.
Building AI Solutions on the Edge
14 HoursThis instructor-led, live training in Botswana (conducted online or on-site) is tailored for intermediate developers, data scientists, and tech enthusiasts aiming to master the practical deployment of AI models on edge devices for a variety of applications.
Upon completing this training, participants will be able to:
- Understand the foundational principles of Edge AI and its key benefits.
- Set up and configure the edge computing environment.
- Develop, train, and optimize AI models for edge deployment.
- Implement practical AI solutions on edge devices.
- Evaluate and improve the performance of edge-deployed models.
- Address ethical and security considerations in Edge AI applications.
Building Secure and Resilient Edge AI Systems
21 HoursThis instructor-led, live training in Botswana (online or onsite) targets advanced-level cybersecurity professionals, AI engineers, and IoT developers who aim to implement robust security measures and resilience strategies for Edge AI systems.
Upon completion of this training, participants will be capable of:
- Identifying security risks and vulnerabilities inherent in Edge AI deployments.
- Implementing encryption and authentication techniques to protect data.
- Designing resilient Edge AI architectures capable of withstanding cyber threats.
- Applying secure AI model deployment strategies within edge environments.
CANN for Edge AI Deployment
14 HoursMigrating CUDA Applications to Chinese GPU Architectures
21 HoursEdge AI for Agriculture: Smart Farming and Precision Monitoring
21 HoursThis instructor-led, live training in Botswana (online or onsite) targets beginner to intermediate-level agritech professionals, IoT specialists, and AI engineers eager to develop and deploy Edge AI solutions for smart farming.
Upon completion of this training, participants will be equipped to:
- Grasp the significance of Edge AI in precision agriculture.
- Implement AI-driven monitoring systems for crops and livestock.
- Develop automated irrigation and environmental sensing solutions.
- Enhance agricultural efficiency through real-time Edge AI analytics.
Edge AI in Autonomous Systems
14 HoursThis instructor-led, live training in Botswana (online or onsite) is aimed at intermediate-level robotics engineers, autonomous vehicle developers, and AI researchers who wish to leverage Edge AI for innovative autonomous system solutions.
By the end of this training, participants will be able to:
- Understand the role and benefits of Edge AI in autonomous systems.
- Develop and deploy AI models for real-time processing on edge devices.
- Implement Edge AI solutions in autonomous vehicles, drones, and robotics.
- Design and optimize control systems using Edge AI.
- Address ethical and regulatory considerations in autonomous AI applications.
Edge AI: From Concept to Implementation
14 HoursThis instructor-led, live training in Botswana (online or on-site) is designed for intermediate-level developers and IT professionals seeking a comprehensive understanding of Edge AI, from initial concepts to practical implementation, including setup and deployment.
Upon completing this training, participants will be able to:
- Grasp the fundamental concepts of Edge AI.
- Set up and configure Edge AI environments.
- Develop, train, and optimise Edge AI models.
- Deploy and manage Edge AI applications.
- Integrate Edge AI with existing systems and workflows.
- Address ethical considerations and best practices in Edge AI implementation.
Edge AI for Computer Vision: Real-Time Image Processing
21 HoursThis instructor-led, live training in Botswana (online or onsite) is designed for intermediate to advanced computer vision engineers, AI developers, and IoT professionals who wish to implement and optimise computer vision models for real-time processing on edge devices.
Upon completion of this training, participants will be able to:
- Grasp the fundamentals of Edge AI and its applications in computer vision.
- Deploy optimised deep learning models on edge devices for real-time image and video analysis.
- Utilise frameworks such as TensorFlow Lite, OpenVINO, and NVIDIA Jetson SDK for model deployment.
- Optimise AI models for performance, power efficiency, and low-latency inference.
Edge AI for Financial Services
14 HoursThis instructor-led, live training in Botswana (online or onsite) is aimed at intermediate-level finance professionals, fintech developers, and AI specialists who wish to implement Edge AI solutions in financial services.
By the end of this training, participants will be able to:
- Understand the role of Edge AI in financial services.
- Implement fraud detection systems using Edge AI.
- Enhance customer service through AI-driven solutions.
- Apply Edge AI for risk management and decision-making.
- Deploy and manage Edge AI solutions in financial environments.
Edge AI for Healthcare
14 HoursThis instructor-led, live training in Botswana (online or onsite) is aimed at intermediate-level healthcare professionals, biomedical engineers, and AI developers who wish to leverage Edge AI for innovative healthcare solutions.
By the end of this training, participants will be able to:
- Understand the role and benefits of Edge AI in healthcare.
- Develop and deploy AI models on edge devices for healthcare applications.
- Implement Edge AI solutions in wearable devices and diagnostic tools.
- Design and deploy patient monitoring systems using Edge AI.
- Address ethical and regulatory considerations in healthcare AI applications.
Edge AI in Industrial Automation
14 HoursThis instructor-led, live training in Botswana (online or onsite) is designed for intermediate-level industrial engineers, manufacturing professionals, and AI developers who wish to implement Edge AI solutions in industrial automation.
Upon completion of this training, participants will be able to:
- Comprehend the role of Edge AI in industrial automation.
- Deploy predictive maintenance solutions using Edge AI.
- Apply AI techniques for quality control within manufacturing processes.
- Optimise industrial processes using Edge AI.
- Deploy and manage Edge AI solutions in industrial environments.
