Can Edge Devices Run Multiple AI Models in Parallel?

With the rise of IoT, industrial automation, and edge computing, the AI tasks run on edge devices are becoming increasingly complex. In real-world scenarios such as video analytics in smart cities, vehicle perception in autonomous driving, or device monitoring in industrial IoT, a single device may need to run multiple AI models simultaneously. So, can edge devices run multiple models in parallel? And how are the limited hardware resources managed? This article explores these questions and key solutions.

1. Can Edge Devices Run Multiple AI Models in Parallel?

Modern edge devices are capable of running multiple AI models, thanks to advancements in hardware architecture and sophisticated software support. However, the complexity of running multiple models depends on the following key factors:

A. Hardware Capabilities
Multi-core processors (e.g., Arm Cortex series) and hardware accelerators such as GPUs, NPUs, and TPUs enable parallel task allocation across distinct computing units.

Memory Capacity and Bandwidth
Running multiple models in parallel requires ample memory buffering. This is especially critical when performing simultaneous tasks like image processing and natural language processing.

B. Model Complexity
The size and computational demands of models directly impact their feasibility for parallel execution. If a model demands excessive computation, it may monopolize resources, affecting the execution of other tasks.

C. Optimization Techniques
Optimized models (e.g., quantized or compressed models) are more suitable for parallel deployment because they consume fewer computational and memory resources. Advanced optimization techniques can significantly reduce resource overhead.

2. How Is Computational Resource Allocation Managed?

To efficiently run multiple AI models and maximize edge device utilization, computational resources must be allocated effectively. This is typically achieved through the following methods:

A. Scheduling Algorithms

Priority Scheduling:
Resources are allocated based on task importance and real-time requirements, e.g., prioritizing real-time perception tasks in autonomous driving.

Time-slicing:
By allocating computing resources to different time slices, multiple models can run in turns.

B. Dynamic Resource Reallocation
Edge devices can dynamically adjust resource allocation based on real-time requirements. For instance, when one model is idle, its resources can be reallocated to another task.

C. Hardware-specific Optimization

Hardware Acceleration
Using hardware accelerators (e.g., CUDA, TensorRT, or NPU) enhances task efficiency, enabling multiple models to run simultaneously.

Memory Allocation Management
Unified memory management (e.g., ONNX Runtime) optimizes memory sharing among models, reducing resource contention.

D. Multi-model Execution Frameworks

Certain frameworks specialize in optimizing parallel execution of multiple models, including resource management and scheduling functionalities:

1. TensorFlow Serving:
Supports dynamic deployment and inference of multiple deep learning models.
2. Nvidia Triton Inference Server:
Designed for multi-model deployment, capable of efficiently allocating resources for complex applications.
3. ONNX Runtime:
Used for cross-platform optimization and running multiple ONNX models, compatible with a variety of environments.

3. Real-World Applications of Multi-model Execution

A. Smart Cities
Runs image classification models (for traffic monitoring) and people detection models (for public safety) simultaneously.

B. Industrial IoT
A single device can simultaneously run predictive maintenance models and energy consumption optimization models, boosting factory operation efficiency.

C. Autonomous Vehicles
Processes object detection models and path planning models in real time to ensure driving safety and navigation efficiency.

4. Key Considerations When Running Multiple Models

When deploying multi-model tasks, consider the following:

1. Model Optimization
Ensure models are optimized through techniques such as pruning and quantization to reduce hardware strain.
2. Hardware Selection
Select devices with ample memory and robust parallel computation capabilities.
3. Framework Compatibility
Ensure the deployment framework supports multi-model scheduling and hardware optimization.

Enabling Multi-AI Model Execution on Edge Devices

The ability of edge devices to run multiple AI models opens new possibilities for complex IoT and industrial AI scenarios. This process relies on efficient hardware architecture, intelligent scheduling algorithms, and specialized optimization frameworks. With support from advanced technologies, edge AI devices can not only run multiple tasks but also deliver low-latency and highly efficient intelligent services.

As an edge device manufacturer, our products are compatible with mainstream AI frameworks and optimization tools to meet the needs of multi-model execution, empowering growth in industrial IoT, smart cities, and beyond.