aZen
  • About aZen
  • Introduction
  • Our Mission
  • How aZen Provides Value?
  • Home Page
  • ✨aZen Protocol
    • Introduction
    • How Does aZen Protocol Reshape Decentralized Computing?
    • Core Architecture & Key Functions
    • Application and use cases
    • Computing Architecture & Incentive Mechanism
  • ⚙️aZen Architecture
    • Overview
    • Product Overview
    • aZen DeFAI
    • SocialFi
  • 🏆aZen Hub
    • Introduction
    • Earning Center
    • Social AI Agent
    • $XaZen Pre-Mining & TGE
    • How To Get Started
    • Daily Check-in
    • Referral Rewards
    • ZenHive Rush
  • 💎aZen DePIN
    • Introduction
    • aZen DePIN Lite
  • 🖥️ZenHive
    • Introduction
    • Real-World Solution (RWS) Approach
    • ZenHive Hardware Products & Architecture
    • AI & Data Analytics Capabilities
    • ZenHive’s Target Industries
    • ZenHive’s Business Model
    • ZenHive - A Leader in DePIN AI Computing and Data Intelligence
    • ZenHive Testnet Mining
  • 💹POC Tokenomics
    • Introduction
    • $AZEN Utility
    • $AZEN Allocation
    • Proof of Contribution (PoC)
      • Token Emission
      • Proof of Contribution Categories
        • Computation PoC
        • Delegate PoC
        • Aggregate PoC
        • Data Center PoC
        • Service Delivery PoC
      • Node Operators
  • 📚Resources
    • Brand Story
    • Roadmap
    • Our Team
    • FAQ
Powered by GitBook
On this page
  1. aZen Protocol

Computing Architecture & Incentive Mechanism

aZen Protocol provides a modular, scalable, and autonomous computing environment by integrating dfNFTs, AI agents, and smart contract-driven orchestration.

  • dfNFT-Based Computation & AI Resource Market

    • Users can tokenize, trade, and deploy AI models, applications, and computational power.

    • Smart contract automation ensures optimal pricing and transparent execution.

  • AI Agent-Driven Computation Layer

    • AI Agents predict workload requirements and allocate compute resources dynamically.

    • Smart contract-based automation reduces inefficiencies and ensures optimal utilization.

  • Privacy-Preserving AI Computation

    • Zero-knowledge proofs (ZKP) and homomorphic encryption ensure AI computations remain secure and verifiable.

    • Privacy-preserving ML (PPML) enables decentralized AI model training while maintaining data privacy.

  • AI-Optimized Tokenomics & Incentive Models

    • AI-driven models dynamically adjust staking, token pricing, and compute resource allocation.

    • Users contributing computational power, AI services, or dApps are rewarded via tokenized incentives.

PreviousApplication and use casesNextOverview

Last updated 2 months ago

✨