Edge AI Architecture: Designing for On-Device Intelligence

Edge AI requires careful architectural decisions to balance capability, latency, and resource constraints.

Edge AI Design Patterns

# edge_ai_architecture.py - Patterns for edge AI systems

from dataclasses import dataclass
from typing import Optional, Callable
from enum import Enum
import asyncio

class ProcessingTier(Enum):
    DEVICE = "device"      # On-device processing
    EDGE = "edge"          # Edge server/gateway
    CLOUD = "cloud"        # Cloud processing

@dataclass
class EdgeConfig:
    model_path: str
    max_latency_ms: int
    fallback_enabled: bool
    sync_interval_minutes: int

class EdgeAIOrchestrator:
    """Orchestrate AI across device, edge, and cloud."""

    def __init__(self, device_model, edge_endpoint: str, cloud_endpoint: str):
        self.device_model = device_model
        self.edge_endpoint = edge_endpoint
        self.cloud_endpoint = cloud_endpoint

    async def process(self, input_data, requirements: dict) -> dict:
        """Process with intelligent tier selection."""
        tier = self.select_tier(input_data, requirements)

        try:
            if tier == ProcessingTier.DEVICE:
                return await self.process_device(input_data)
            elif tier == ProcessingTier.EDGE:
                return await self.process_edge(input_data)
            else:
                return await self.process_cloud(input_data)
        except Exception as e:
            # Fallback chain
            return await self.fallback_process(input_data, tier, e)

    def select_tier(self, input_data, requirements: dict) -> ProcessingTier:
        """Select processing tier based on requirements."""
        complexity = self.estimate_complexity(input_data)
        max_latency = requirements.get("max_latency_ms", float("inf"))
        privacy_required = requirements.get("privacy_required", False)

        if privacy_required:
            return ProcessingTier.DEVICE

        if complexity < 0.3 and max_latency < 50:
            return ProcessingTier.DEVICE
        elif complexity < 0.7 or max_latency < 200:
            return ProcessingTier.EDGE
        else:
            return ProcessingTier.CLOUD

    async def process_device(self, input_data) -> dict:
        """Process entirely on device."""
        result = self.device_model.predict(input_data)
        return {"tier": "device", "result": result, "latency_ms": self.last_latency}

    async def sync_model(self):
        """Sync local model with cloud updates."""
        # Download updated model weights
        new_weights = await self.download_model_update()
        if new_weights:
            self.device_model.load_weights(new_weights)


class HybridInference:
    """Combine local and cloud inference for best results."""

    def __init__(self, local_model, cloud_client):
        self.local = local_model
        self.cloud = cloud_client

    async def speculative_inference(self, input_data) -> dict:
        """Use local model speculatively, verify with cloud if uncertain."""
        # Get local prediction with confidence
        local_result = self.local.predict_with_confidence(input_data)

        if local_result["confidence"] > 0.9:
            return {"result": local_result["prediction"], "source": "local"}

        # Uncertain - verify with cloud
        cloud_result = await self.cloud.predict(input_data)
        return {"result": cloud_result, "source": "cloud"}

    async def ensemble_inference(self, input_data) -> dict:
        """Combine local and cloud predictions."""
        local_task = asyncio.create_task(self.local.predict_async(input_data))
        cloud_task = asyncio.create_task(self.cloud.predict(input_data))

        local_result, cloud_result = await asyncio.gather(
            local_task, cloud_task, return_exceptions=True
        )

        return self.combine_results(local_result, cloud_result)

Edge AI architecture balances latency, privacy, and capability across processing tiers.