1. End Devices:
- Data Collection and Initial Processing: End devices, such as sensors, actuators, or mobile devices, collect raw data and perform initial preprocessing tasks like filtering, noise reduction, or basic feature extraction.
- Lightweight DNN Layers: These devices can host early layers of a DNN that are less computationally intensive, such as convolutional layers for image recognition or simple feature extraction layers.
- Edge Offloading: When computational demands exceed device capabilities or real-time processing is crucial, data and partial results can be offloaded to edge nodes for further processing.
2. Edge Nodes:
- Complementary Processing: Edge nodes, located closer to end devices, provide intermediate processing power and storage.
- Intermediate DNN Layers: Edge nodes can host more complex DNN layers, such as deeper convolutional layers, recurrent layers for sequential data, or initial decision-making layers.
- Local Inference and Decision-Making: They can perform inference tasks on locally collected data, reducing latency and network traffic.
- Cloud Offloading: For tasks requiring extensive computational resources or access to larger datasets, edge nodes can offload data and partial results to the cloud.
3. Cloud Data Centers:
- Centralized Hub: Cloud data centers offer vast computational resources, storage, and access to large-scale datasets.
- Complex DNN Layers: They host the most computationally demanding layers of a DNN, such as fully connected layers, attention mechanisms, or large language models.
- Model Training and Refinement: Cloud resources are used for training and refining DNN models using extensive datasets.
- Global Insights and Knowledge Sharing: Cloud-based models can aggregate insights from multiple edge devices and provide a global perspective for decision-making.
Benefits of Distributed DNN Architecture:
- Reduced Latency: Processing data closer to the source minimizes delays, essential for real-time applications.
- Bandwidth Conservation: Less data transmission to the cloud reduces network traffic and costs.
- Improved Privacy and Security: Sensitive data can be processed locally, reducing exposure to security risks.
- Enhanced Scalability: Edge nodes can handle increasing workloads, reducing reliance on centralized cloud infrastructure.
- Adaptability to Diverse Deployment Scenarios: The distribution can be tailored to specific network conditions, device capabilities, and application requirements.
Key Considerations for Effective Distribution:
- Model Segmentation: Strategically dividing DNN layers across devices based on computational requirements and communication constraints.
- Model Compression and Pruning: Reducing model size and complexity for deployment on resource-constrained devices.
- Communication Optimization: Efficient data transfer and model updates between devices, potentially using techniques like federated learning.
- Resource Management: Balancing workload distribution and computational resources across devices to optimize performance and energy efficiency.