Hands-on workshop: Huawei Ascend NPU embedded development
Four days on the Atlas 200i DK A2 and OrangePi AIpro (8T) — from first boot to running vision, speech and generative models on the NPU.
Workshop overview
This hands-on workshop gives practical experience with the Huawei Atlas 200i DK A2, powered by the Ascend 310B chip. Participants gain an in-depth understanding of the Da Vinci architecture used in Huawei Neural Processing Units, with the emphasis on Python-based development and real-world AI deployment on embedded hardware.
It equips participants with industry-relevant skills in edge AI development on Ascend NPUs, enabling deployment beyond traditional GPU-based systems. We run it for universities, research groups and engineering teams across Pakistan, on your site or ours.
- Duration
- 3 days, plus an optional 4th
- Hardware
- Atlas 200i DK A2
OrangePi AIpro (8T) - Format
- Hands-on, one kit per participant or pair
- Prerequisites
- Working Python; no NPU experience needed
- Delivery
- Rawalpindi / Islamabad, or on site
Learning outcomes
- Understand the Ascend ecosystem and the Da Vinci architecture
- Configure and operate the Atlas 200i Developer Kit
- Run AI inference using Python and Jupyter
- Convert and deploy models using the Ascend Tensor Compiler (ATC)
- Deploy vision, speech and image enhancement models on edge hardware
Schedule and contents
Goal: Understand Ascend hardware, the Da Vinci architecture, and build a working development system.
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Session 1Introduction to Ascend 200i DK A2 / OrangePi AIpro (8T)
- Ascend ecosystem overview
- Edge AI use cases and application domains
- Ascend product family overview
- Edge versus cloud Ascend devices
- Conceptual comparison with GPU and Jetson platforms
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Session 2Physical setup & I/O interfaces
- Power and thermal considerations
- HDMI, USB and Ethernet connectivity
- Camera, sensor and storage options
- First boot and hardware validation
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Session 3Da Vinci architecture deep dive
- AI Core, Vector and Cube engines
- Memory hierarchy and data types (FP16 / INT8)
- Differences between Ascend NPUs and GPUs
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Session 4OS installation & network setup
- Ubuntu / openEuler installation
- SD card image creation
- Network, SSH and remote access configuration
- Launching the Jupyter Notebook environment
Goal: Access the system, understand the runtime architecture, and run your first AI inference.
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Session 5System access & navigation
- User accounts and permissions
- SSH login and file system navigation
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Session 6Jupyter Notebook workflow
- Notebook structure and lab organisation
- Verifying Ascend runtime availability
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Session 7Image classification — ResNet
- ImageNet classification pipeline
- Running inference and testing custom images
- Measuring inference performance
Goal: Convert models and deploy multiple AI modalities.
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Session 8Ascend Tensor Compiler (ATC)
- ONNX to OM model conversion
- Hardware-aware optimisation parameters
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Session 9Object detection — YOLOv5
- YOLO architecture overview
- Bounding box visualisation and inference
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Session 10OCR & speech recognition
- OCR pipeline and text extraction
- Speech-to-text inference using ASR models
Goal: Run advanced vision pipelines and deployment-style demos.
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Optional dayAdvanced pipelines
- Image enhancement and HDR reconstruction
- Custom classification use cases
- Style transfer and GAN-based applications
- Medical and scientific AI inference
- Semantic segmentation and portrait enhancement
- YOLO inference on video and live camera streams
Download the workshop brochure
A two-page PDF with the overview, learning outcomes and the full day-by-day schedule — useful for circulating to a department or attaching to an approval request.
Book this workshop for your team
Tell us the cohort size and your preferred dates. We supply the boards, run the sessions, and leave the hardware and the know-how with you.
Enquire now