Huawei HCIE-AI case studies
Huawei HCIE-AI · Put your knowledge to work in context.
HCIE-AI sits at the Expert tier of Huawei Career Certification, the top of a three-level structure that runs from Huawei Certified ICT Associate (HCIA) through Huawei Certified ICT Professional (HCIP) to Huawei Certified ICT Expert (HCIE). This page is an independent study resource for candidates preparing that exam; it is not affiliated with, endorsed by, or operated by Huawei. The written exam is delivered through Pearson VUE, and Huawei notes that requirements and processes differ by level and technical field, so always confirm the current syllabus, question count, duration and fee on the official certification site before booking. The published domain breakdown used here splits the blueprint into four equally weighted areas: AI fundamentals and machine learning; deep learning and neural network architectures; the Huawei Ascend AI computing platform and CANN; and AI application development and deployment. The Huawei HCIE-AI case studies are 5 cases and about 2,600 words.
1Scaling stall on an 8-node Ascend 910B training cluster
An 8-node training job runs on Atlas 800T A2 servers, each with 8 Ascend 910B NPUs and two 200G RoCE NICs. The model is a 7B-parameter dense transformer trained in BF16 with data parallelism only.
581 words, about 3 min2AMP accuracy regression after porting ConvNeXt to Ascend
A team ports an internal image classifier from A100 to Ascend 910B using torch npu and CANN 7.0. The model is a ConvNeXt-B with LayerNorm, GELU, and dynamic input sizes from 512 to 1024 px.
527 words, about 3 min3Video analytics appliance bottlenecked before the NPU
An inspection appliance uses an Atlas 300I Duo inference card to analyze 16 RTSP 1080p streams with a YOLOv5s detector at 640x640. The requirement is 25 FPS per stream.
494 words, about 2 min4Wafer-defect classifier with high validation PR-AUC but poor line recall
A semiconductor fab pilots a wafer-map defect classifier. The training set contains 120,000 die images, of which 0.9% are defective.
493 words, about 2 min532K-context fine-tuning OOM on an 8-NPU node
A team is fine-tuning a 7B-parameter transformer on one node with 8 Ascend 910B NPUs, each with 64 GB HBM. The task uses 32K-token sequences, micro-batch 1, and gradient checkpointing.
505 words, about 3 min