🧬 Architecture

NeuraLink is a modular, decentralized AI infrastructure driven by smart contracts and powered by community-run compute nodes. The system is designed for openness, verifiability, composability, and multi-layer integration.

4.1 System Overview

NeuraLink’s architecture is divided into four primary layers:

Layer
Components
Description

User Interface Layer

Task Portal / Model Viewer / Public APIs

Entry point for participants and external applications

Execution Layer

Simulation Engine / Reward Layer / Governance

Core operations for training, incentives, and protocol coordination

Storage Layer

Data Vault / IPFS / Arweave / Filecoin

Secure, decentralized storage of datasets, models, logs

On-Chain Logic Layer

Task Contracts / Data Proof Contracts / Model IDs

Smart contracts governing task logic, data ownership, and model tracking

4.2 Communication Workflow (Simplified)

  1. User uploads data → stored via decentralized networks (e.g. Arweave)

  2. Training task initiated → by user or platform, logged on-chain

  3. Nodes execute task → local training and parameter updates

  4. Nodes submit proof → results validated by verifiers

  5. Smart contract distributes rewards → model version recorded on-chain

4.3 Technical Stack Overview

Layer / Function
Technology Choices

Blockchain Layer

BNB Chain / EVM-compatible L2s

Storage Layer

Arweave / IPFS / Filecoin

Smart Contract Layer

Solidity / Vyper

Frontend / API

Next.js / Ethers.js / GraphQL

Training Frameworks

ONNX Runtime / TensorFlow / PyTorch

Cryptographic Tools

ZK-SNARKs / FHE (Fully Homomorphic Encryption)

Communication Layer

libp2p / WebSocket / JSON-RPC

4.4 Model Versioning System

Each model is tracked on-chain with a unique ModelID and CommitHash. Every update logs the following:

  • Parameter deltas (e.g. weight updates)

  • Training data hashes

  • Validation metrics

  • Node signatures

  • Fork/Merge/Rollback lineage

This ensures reproducibility, traceability, and collaborative model evolution.

4.5 Security & Scalability Design

  • Training Verification: Leverages TEE (Trusted Execution Environment) or ZK Proofs for compute integrity

  • Access Control: Role-based permissioning on contracts with least-privilege principles

  • Anti-Sybil Defense: Task-specific verification, proof redundancy, and identity-weighted incentives

  • Composable APIs: Developer-friendly endpoints for embedding simulation into external dApps

  • Multi-chain Support: Future compatibility with other L2s, DePIN protocols, and model marketplaces

Previous🔧 Core ComponentsNext🚀 Incentive System

Last updated