AI-Centric System Architecture

An AI-Centric System Architecture is a software system architecture for AI-driven systems (uses artificial intelligence as core processing components).

• Context:
  • It can process System Input through AI processing systems.
  • It can manage System State through AI state managements.
  • It can handle System Integration through AI interfaces.
  • ...
  • It can range from being a Basic AI System Architecture to being an Advanced AI System Architecture, depending on its processing capability.
  • It can range from being a Single Domain System Architecture to being a Multi-Domain System Architecture, depending on its application scope.
  • It can range from being a Standalone AI System Architecture to being a Distributed AI System Architecture, depending on its deployment model.
  • ...
  • It can integrate with Data Storage System for information management.
  • It can connect to Model Repository System for AI deployment.
  • It can support Monitoring System for performance tracking.
  • ...
• Examples:
  • AI Processing Type System Architectures, such as:
    • Language Processing System Architectures, such as:
      • LLM-Centric System Architecture for language model processing.
      • NLP System Architecture for text analysis.
    • Vision Processing System Architectures, such as:
      • Computer Vision System Architecture for image analysis.
      • Video Processing System Architecture for motion analysis.
    • Multi-Modal System Architectures, such as:
      • Vision-Language System Architecture for cross-modal processing.
      • Audio-Visual System Architecture for multimedia analysis.
  • AI Complexity System Architectures, such as:
    • Basic AI System Architectures, such as:
      • Single Model System Architecture for basic inference.
      • Pipeline System Architecture for sequential processing.
    • Advanced AI System Architectures, such as:
      • Multi-Model System Architecture for ensemble processing.
      • Hybrid System Architecture for mixed processing.
  • AI Deployment System Architectures, such as:
    • Local System Architectures, such as:
      • Edge AI System Architecture for device processing.
      • Embedded AI System Architecture for hardware integration.
    • Cloud System Architectures, such as:
      • Amazon Integration System Architecture for AWS deployment.
      • Microsoft Integration System Architecture for Azure deployment.
  • AI Scale System Architectures, such as:
    • Enterprise System Architectures, such as:
      • Global Distribution System Architecture for worldwide operation.
      • High Availability System Architecture for mission critical tasks.
    • Research System Architectures, such as:
      • Experimental System Architecture for model development.
      • Prototype System Architecture for concept validation.
  • ...
• Counter-Examples:
  • Traditional System Architecture, which lacks AI processing capability.
  • Data Pipeline System Architecture, which lacks intelligent processing.
  • Basic Service System Architecture, which lacks AI integration.
  • Static Processing System Architecture, which lacks learning capability.
• See: Distributed System Architecture, Cloud System Architecture, ML System Architecture, LLM-Centric System Architecture, Neural System Architecture.

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