DeepMind AI
  • Introduction
    • Scope
    • Audience
    • Technical Prerequisites
  • System Architecture
  • Core Components
    • DEEP Engine
  • Technical Specifications
  • $DEEP Economic Model
  • Software Implementation
  • Research and Vision
  • Development Roadmap
  • Security Considerations
  • Integration Guidlines
Powered by GitBook
On this page

Technical Specifications

DeepMind AI Technical Specifications – AGI-Driven Blockchain Architecture

Technical Specifications

DeepMind AI’s technical foundation is engineered for high performance, scalability, and seamless integration across blockchain ecosystems. Below are the critical specifications underpinning the platform:

1. Performance & Scalability

  • Throughput: Processes 1.2 million transactions/hour across 50+ chains, with <500ms latency for real-time alerts.

    • Achieved via Apache Kafka for stream processing and Apache Flink for stateful computations.

    • Kubernetes clusters auto-scale based on demand, leveraging cloud providers (AWS/GCP) and edge nodes.

  • Horizontal Scalability:

    • Dockerized microservices orchestrated via Kubernetes, supporting dynamic pod scaling.

    • GPU/TPU acceleration for AI inference (NVIDIA A100, Google TPU v4).

2. AI/ML Infrastructure

  • Model Training:

    • PyTorch/TensorFlow frameworks with federated learning for privacy-preserving training.

    • Custom GNNs (Graph Neural Networks) for cross-chain transaction graph analysis.

  • Inference Engine:

    • ONNX Runtime for optimized model deployment; <50ms inference latency.

    • Model Zoo: Pre-trained models for fraud detection (F1-score: 0.94), market prediction (MAPE: 2.8%).

3. Blockchain Interoperability

  • Supported Chains:

    • EVM chains (Ethereum, Polygon, BSC) via Web3.js/ethers.js integration.

    • Non-EVM chains (Solana, Cosmos, Bitcoin) using protocol-specific adapters (e.g., CosmWasm, UTXO parsers).

  • Cross-Chain Protocols:

    • LayerZero for omnichain messaging; Wormhole for asset bridging.

    • Atomic Swaps via HTLCs (Hash Time-Locked Contracts) with 2-minute expiry windows.

4. Data Storage & Management

  • Immutable Storage:

    • IPFS/Filecoin for raw data archives; Arweave for permanent intelligence logs.

    • Data Sharding: Horizontal partitioning across 16-node clusters for fault tolerance.

  • Database:

    • Time-series databases (InfluxDB) for real-time analytics; Graph databases (Neo4j) for entity relationships.

    • Encryption: AES-256 for data at rest; TLS 1.3 for in-transit security.

5. APIs & Developer Tools

  • API Gateway:

    • REST/GraphQL endpoints with <100ms response time (cached via Redis).

    • Rate limiting (1,000 requests/min) and OAuth 2.0 authentication.

  • SDKs:

    • Python/JavaScript SDKs with pre-built queries for wallet clustering, risk scoring, and trend analysis.

    • OpenAPI/Swagger documentation with sandbox testnets.

6. Security & Compliance

  • Smart Contract Audits:

    • Quarterly audits by CertiK and OpenZeppelin; formal verification for critical logic.

  • Privacy:

    • zk-SNARKs (Circom/ZoKrates) for private query validation; FHE (Fully Homomorphic Encryption) pilot for encrypted computations.

  • Compliance:

    • OFAC/Sanctions Screening: Integration with Chainalysis and Elliptic datasets.

    • GDPR/CCPA: Data anonymization and right-to-delete workflows.

7. Network & Infrastructure

  • Decentralized Nodes:

    • 1,000+ global nodes across 30 regions, managed via a PoS consensus for IEP validation.

    • Anti-DDoS: Cloudflare Enterprise protection; rate limiting and IP reputation filtering.

  • Monitoring:

    • Prometheus/Grafana dashboards for uptime (99.99% SLA) and resource utilization.

    • ELK Stack (Elasticsearch, Logstash, Kibana) for log aggregation.

8. Tokenomics & Gas Optimization

  • Gas Fees:

    • Batch Transactions: ERC-4337 account abstraction for bundling operations.

    • zk-Rollups: Reduces IEP marketplace gas costs by 80%.

  • $DEEP Staking:

    • Minimum 1,000 $DEEP for data providers; 5% annualized staking rewards.

9. User Experience

  • Analytics Dashboard:

    • React.js frontend with WebGL-powered visualizations (1M+ data points rendered in <1s).

    • Mobile Optimization: Progressive Web App (PWA) with offline functionality.

  • Custom Alerts:

    • Webhook/Telegram integrations; threshold triggers with <2s notification latency.

10. Development & Deployment

  • CI/CD Pipeline:

    • GitHub Actions for automated testing; Argo CD for Kubernetes rollouts.

  • Testing:

    • Chaos Engineering: Gremlin-based failure injection for resilience testing.

    • Smart Contract Testnets: Forked mainnet environments via Tenderly.

By adhering to these specifications, DeepMind AI ensures enterprise-grade reliability, low-latency intelligence delivery, and seamless interoperability across the decentralized web.

PreviousDEEP EngineNext$DEEP Economic Model

Last updated 4 months ago