π§Core Functions Overview
Overview
Neural Network's core functions are designed to create a seamless, efficient, and secure decentralized AI computing ecosystem. Our platform integrates multiple sophisticated systems to deliver comprehensive AI training and inference capabilities.
π₯οΈ Computing Power Aggregation System
π‘ Multi-Source Node Support
Individual Contributors: Personal GPU/CPU resources from developers and enthusiasts
Institutional Partners: Computing resources from organizations and research institutions
Data Centers: Professional-grade infrastructure partnerships
Hybrid Deployments: Mixed resource configurations for optimal performance
β‘ Dynamic Resource Allocation
Real-time computing power assessment and allocation
Intelligent load balancing across nodes
Automatic scaling based on training requirements
Quality assurance through environment detection and compensation algorithms
π€ AI Training Task Distribution and Execution
π Intelligent Task Management
Automatic Task Grouping: Smart categorization of training requests
Optimized Resource Configuration: Matching tasks with appropriate computing power
Distributed Training Support: Seamless coordination across multiple nodes
Pre-deployment Optimization: Testing and debugging before full deployment
π οΈ Framework Compatibility
PyTorch - Full support for dynamic computational graphs
TensorFlow - Complete ecosystem integration
JAX - High-performance numerical computing
HuggingFace Transformers - State-of-the-art NLP models
Stable Diffusion & LoRA Training - Advanced image generation
Whisper / RWKV / LLM Fine-tuning - Specialized model training
βοΈ Blockchain Computing Power Tracking and Settlement
π° Smart Contract Settlement
Automated Payments: Instant settlement upon task completion
Transparent Pricing: Clear cost structure for all participants
Multi-token Support: Flexible payment options including native tokens
π Verification & Auditability
On-chain Result Tracking: All training outputs recorded on blockchain
Full Trace Paths: Complete audit trail from task submission to completion
zk-SNARK Integration: Privacy-preserving verification mechanisms
Security Controls: Intelligent dedicated core control for information security
π― Computing Node Orchestration & Reward Mechanism
π Performance-Based Classification
Computing Power Verification: Hardware capability assessment
Execution Efficiency Monitoring: Real-time performance tracking
Quality Scoring: Comprehensive node evaluation system
Dynamic Orchestration: Intelligent task assignment based on node capabilities
π Fair Reward Distribution
Contribution-Based Rewards: Proportional allocation based on actual contribution
Quality Bonuses: Additional rewards for high-performance nodes
Multi-chain Settlement: Support for single-chain and cross-chain payments
Flexible Token Options: Native tokens or combined currency rewards
π AI Model Library and Community Collaboration
π Shared Model Ecosystem
Community Model Sharing: Open platform for model distribution
Collaborative Training: Joint projects between community members
Model Evaluation: Comprehensive review and rating systems
Retraining Capabilities: Easy model fine-tuning and adaptation
π Specialized Domain Support
Facial Recognition: Advanced computer vision applications
Text Generation: Natural language processing solutions
Video Analysis: Multi-modal content understanding
Custom Domains: Extensible framework for specialized use cases
π System Modules Overview
π§ Module
π― Function Description
π€ Task Publishing System
Supports AI developers in publishing training/inference tasks, setting budgets, defining model structures, and establishing acceptance criteria
π₯οΈ Computing Power Access System
Enables nodes to join the platform through container plugins or dedicated clients, submit device information, and receive task allocations
π§ Intelligent Scheduling System
On-chain scheduling contracts that automatically allocate computing nodes based on task parameters, supporting pluggable scheduling strategies (e.g., reputation priority)
β Decentralized Training Verification
Real-time on-chain snapshots of node training processes with multi-node re-verification, ensuring authentic and reliable results
πΎ Data and Model Storage
Integration with Arweave/IPFS for persistent storage of data and model intermediate results, ensuring long-term accessibility and auditability
π° Incentive and Settlement Module
Smart contract-driven dynamic settlement mechanism, distributing token rewards based on computing time, result quality, reputation weight, and other dimensions
βοΈ Node Governance and Punishment
Automated system for freezing stakes of abnormal behavior nodes and subjecting them to DAO arbitration, maintaining healthy platform operations
These core functions work together to create a robust, scalable, and user-friendly platform that democratizes access to AI computing power while maintaining high standards of security and performance.
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