Industrial AI Intelligent Computing Foundation

Computing Power + Platform + Model Integrated Infrastructure

Functional Overview

For manufacturing enterprises and industrial clusters, we provide a closed-loop industrial AI infrastructure of 'computing power - platform - data - model - security'. It supports Ascend all-in-one machines, CloudMatrix clusters, hybrid cloud, and edge-end collaborative deployment, with built-in model migration tools, data governance platforms, industrial model libraries, and MaaS services.

Customer Pain Points

  • Computing resources are scattered, utilization is low, and they cannot elastically support AI training and inference.
  • Domestic computing power adaptation is difficult, and large model migration cycles are long and costs are high.
  • Lack of unified data and model management platforms makes it difficult to accumulate and reuse AI assets.
  • Production environments have high requirements for real-time performance and security, which public clouds cannot meet.

Solution Advantages

  • Heterogeneous intelligent computing unified scheduling: supports CPU/GPU/NPU heterogeneous resource pools, super-intelligent integrated scheduling, improving utilization by 30%+.
  • Industrial models and toolchains: built-in pre-trained models for industrial quality inspection, equipment prediction, and process optimization, providing full-process tools for data annotation, model training, and inference optimization.
  • Edge-end collaboration and security isolation: supports cloud-edge-end collaborative inference, data does not leave the factory; provides national cryptographic-level security protection and audit capabilities.
  • Fast migration and tuning: provides automated migration tools from TensorFlow/PyTorch to Ascend CANN, and operator-level performance tuning services.

Architecture Diagram

Architecture Diagram