7. AI Infrastructure & Emerging Tech Reference Architecture - stanlypoc/AIRA GitHub Wiki

AI Infrastructure & Emerging Tech Reference Architecture

1. Introduction

1.1 Purpose

This document provides initial guidance for AI infrastructure supporting emerging technologies (quantum, neuromorphic, photonic, etc.). Note this is a rapidly evolving field - architectures may require significant updates as new research emerges.

1.2 Audience

  • Cloud Architects
  • AI Infrastructure Engineers
  • Emerging Tech Teams
  • Compliance Officers

1.3 Scope & Applicability

In Scope:

  • Hybrid compute orchestration
  • Specialized hardware management
  • Multi-modal data pipelines

Out of Scope:

  • Chip-level design
  • Proprietary quantum algorithms

1.4 Assumptions & Constraints

Prerequisites:

  • Kubernetes 1.26+
  • Infrastructure-as-Code expertise

Technical Constraints:

  • Minimum 100GbE networking
  • Co-location requirements for quantum-classical systems

Ethical Boundaries:

  • No dual-use military applications
  • Physical safety interlocks for robotics

1.6 Example Models

Level Traditional Tech Emerging Tech
Level 2 TensorFlow/PyTorch Quantum Kernels (PennyLane)
Level 3 Ray Cluster Photonic NN (Lightmatter)
Level 4 Federated Learning System Neuromorphic Chips (Loihi)

2. Architectural Principles

Here are the core Architecture Principles for AI Infrastructure & Emerging Technologies, crafted to guide organizations in designing scalable, secure, and future-proof platforms that support cutting-edge AI workloads such as LLMs, AutoML, federated learning, and quantum ML:

⚙️2.1 Foundational Architecture Principles for AI Infrastructure & Emerging Technologies

1. Composable Architecture

Design infrastructure as modular, plug-and-play components.

  • Enable mixing of compute, storage, orchestration, and observability tools.
  • Use standardized APIs (e.g., REST, gRPC, MLflow) and interfaces (e.g., ONNX, Hugging Face).
  • Support open source and proprietary interoperability.

2. Hardware Abstraction & Optimization

Abstract underlying hardware choices while enabling optimization.

  • Support heterogeneous compute: CPUs, GPUs, TPUs, FPGAs.
  • Optimize workloads via auto-scaling and right-sized instance selection (e.g., NVIDIA A100 vs. T4).
  • Use infrastructure-aware compilers: XLA, TVM, Triton.

3. Cloud-Native & Hybrid Deployment

Architect for cloud, on-prem, and edge environments.

  • Use Kubernetes and containerized ML workflows (e.g., KubeFlow, Vertex AI, SageMaker).
  • Leverage Azure Arc, AWS Outposts, or Anthos for hybrid/edge extensions.
  • Enable dynamic workload migration and failover.

4. Scalable MLOps Foundation

Provide first-class support for full ML lifecycle management.

  • Integrate CI/CD pipelines, model registries, feature stores, and model monitors.
  • Automate testing, rollout, and rollback.
  • Tools: MLflow, Metaflow, Feast, Airflow, Argo.

5. Multi-Tenancy and Isolation

Ensure resource, data, and identity isolation across teams and environments.

  • Use namespaces, projects, and quota management in Kubernetes or cloud environments.
  • Enforce RBAC, VPC segmentation, and zero-trust access models.
  • Enable safe collaboration without data leakage.

6. Data Lineage & Metadata Management

Track end-to-end lineage of data and models.

  • Integrate data cataloging tools (e.g., Amundsen, Purview, DataHub).
  • Enable reproducibility with metadata logging (e.g., input schema, model version, training code hash).
  • Store audit trails and model cards automatically.

7. Security & Compliance by Default

Embed privacy, security, and regulatory controls from the ground up.

  • Use encrypted storage, token-based access, and identity federation.
  • Enforce policies via infrastructure-as-code (e.g., Terraform + Sentinel, Azure Policies).
  • Align with GDPR, HIPAA, SOC2, and AI Act as applicable.

8. Observability & Intelligent Monitoring

Build for end-to-end visibility of models, pipelines, and infrastructure.

  • Log metrics (e.g., latency, throughput, resource usage) and events.
  • Integrate with Prometheus, Grafana, OpenTelemetry, or custom dashboards.
  • Apply anomaly detection on system health and model drift.

9. Support for Next-Gen AI Workloads

Future-proof systems to support LLMs, federated learning, quantum ML, etc.

  • Provide GPU and HPC clusters for large model training.
  • Enable federated orchestration (e.g., Flower, NVIDIA FLARE).
  • Prepare for post-classical compute via frameworks like PennyLane or Qiskit.

10. Cost Optimization and Sustainability

Design for cost efficiency and energy-aware computing.

  • Use spot/ephemeral compute where applicable (e.g., model training, experiments).
  • Monitor energy consumption and CO₂ impact (e.g., CodeCarbon).
  • Autoscale idle resources and shut down unused services.

11. Experimentation Velocity

Empower teams to rapidly experiment and deploy models.

  • Provide self-service environments (e.g., notebooks, sandbox clusters).
  • Integrate hyperparameter tuning and AutoML tools.
  • Use lineage tools to roll back, branch, or compare experiments.

12. Responsible Innovation

Ensure ethical development, traceability, and risk management for emerging tech.

  • Encourage model interpretability (e.g., SHAP, LIME).
  • Define red lines (e.g., facial recognition, surveillance, bias) as guardrails.
  • Enable external audits and safety review processes.

2.2 Standards Compliance

  1. Security & Privacy

    • Must comply with: FIPS 140-3, ISO/IEC 27001:2022
    • Practical tip: Hardware root of trust for model weights

  2. Ethical AI

    • Key standards: IEEE 7000-2021, EU AI Act Annex III
    • Checklist item: Energy consumption impact assessment

2.3 Operational Mandates

5 Golden Rules:

  1. Zero-trust between compute planes
  2. Sub-10ms failover for critical components
  3. Physical/logical airgap for protected workloads
  4. Immutable infrastructure for training clusters
  5. Carbon-aware scheduling

Sample Audit Log:

{
  "timestamp": "2023-11-20T14:23:12Z",
  "workload": "quantum-classical hybrid",
  "location": "Azure Quantum EastUS2",
  "energy_consumed_kWh": 42.7,
  "carbon_offset": true
}

3. Architecture by Technology Level

3.1 Level 2 (Basic) - Traditional Accelerated Computing

Definition:
GPU/TPU-based infrastructure for conventional AI workloads

Key Traits:

  • Fixed architecture accelerators
  • Single workload type per cluster
  • Manual scaling

Logical Architecture:

graph LR
    A[Data Lake] --> B[Preprocessing]
    B --> C[Training Cluster]
    C --> D[Model Registry]
    D --> E[Inference Endpoints]

Cloud Implementations:

Provider Services Specialized HW
Azure NDv5 VMs NVIDIA H100
AWS P4d Trainium Chips
GCP A3 VMs TPU v4

3.2 Level 3 (Advanced) - Heterogeneous Computing

Definition:
Orchestrated mix of traditional and emerging compute

Key Traits:

  • Dynamic workload placement
  • Cross-technology interoperability
  • Energy-aware scheduling

Logical Architecture:

graph LR
    A[Workload Manager] --> B[GPU Pool]
    A --> C[Quantum Simulator]
    A --> D[Neuromorphic Grid]
    B & C & D --> E[Unified Results]

Critical Components:

  • Compute capability registry
  • Hybrid scheduler
  • Cross-technology debugger

3.3 Level 4 (Autonomous) - Self-Optimizing Infrastructure

Definition:
Infrastructure that reconfigures based on workload demands

Key Traits:

  • Real-time topology adjustment
  • Predictive provisioning
  • Physical reconfiguration (optical switching)

Logical Architecture:

graph LR
    A[Workload] --> B[Topology Optimizer]
    B --> C[FPGA Fabric]
    B --> D[Quantum Coprocessor]
    B --> E[Optical Network]
    C & D & E --> F[Autoscaling Manager]

Safety Mechanisms:

  • Thermal overload protection
  • Quantum decoherence monitoring
  • Neuromorphic spike rate limiting

4. Glossary & References

Terminology:

  • Bespoke Silicon: Custom AI accelerator ASICs
  • Cryo-Computing: Superconducting systems operating at <4K

References:

  1. MLPerf Infrastructure Benchmark
  2. NIST SP 1500-203 (Quantum-Hybrid Architectures)