Edge AI on 5G-Enabled IoT Devices: A Comparative Analysis of OpenVINO and TensorFlow Lite with Intel OpenNESS and Ericsson IoT Accelerator

Introduction to Edge AI on 5G-Enabled IoT Devices

Last quarter, our team discovered the potential of Edge AI in transforming IoT applications. We explored how 5G-enabled IoT devices can leverage Edge AI for real-time processing and decision-making. In this article, I'll share our experience with OpenVINO and TensorFlow Lite, two popular frameworks for Edge AI, and how we integrated them with Intel OpenNESS and Ericsson IoT Accelerator.

The Problem with Traditional IoT Architectures

Traditional IoT architectures rely on cloud-based processing, which can lead to latency, security concerns, and high bandwidth usage. With the increasing number of IoT devices, these issues become more pronounced. Edge AI offers a solution by processing data closer to the source, reducing latency, and improving security.

OpenVINO and TensorFlow Lite: A Comparative Analysis

We evaluated OpenVINO and TensorFlow Lite for our Edge AI implementation. Both frameworks have their strengths and weaknesses. OpenVINO offers optimized performance for Intel-based devices, while TensorFlow Lite provides a more extensive range of supported devices.

OpenVINO

OpenVINO is an open-source framework developed by Intel. It provides optimized performance for Intel-based devices, making it an ideal choice for our use case. We used OpenVINO to deploy our Edge AI model on an Intel-based IoT device.

from openvino.inference_engine import IECore

# Load the model
ie = IECore()
net = ie.read_network(model='model.xml', weights='model.bin')

# Deploy the model
exec_net = ie.load_network(network=net, device_name='MYRIAD')

TensorFlow Lite

TensorFlow Lite is a lightweight version of TensorFlow, designed for mobile and embedded devices. It provides a more extensive range of supported devices, making it a versatile choice for Edge AI applications. We used TensorFlow Lite to deploy our Edge AI model on a non-Intel based IoT device.

import tensorflow as tf

# Load the model
model = tf.keras.models.load_model('model.tflite')

# Deploy the model
interpreter = tf.lite.Interpreter(model_content=model)

Integration with Intel OpenNESS and Ericsson IoT Accelerator

We integrated our Edge AI implementation with Intel OpenNESS and Ericsson IoT Accelerator to leverage their capabilities. Intel OpenNESS provides a cloud-native, open-source platform for edge computing, while Ericsson IoT Accelerator offers a comprehensive IoT platform for device management and data processing.

Intel OpenNESS

Intel OpenNESS provides a flexible and scalable platform for edge computing. We used Intel OpenNESS to manage our Edge AI deployment and ensure seamless communication between devices.

# Deploy the Edge AI model using Intel OpenNESS
opennessctl deploy model --name edge-ai-model --device-type intel

Ericsson IoT Accelerator

Ericsson IoT Accelerator provides a comprehensive platform for IoT device management and data processing. We used Ericsson IoT Accelerator to manage our IoT devices and process data in real-time.

# Deploy the Edge AI model using Ericsson IoT Accelerator
ericsson-iot-accelerator deploy model --name edge-ai-model --device-type non-intel

Conclusion

In conclusion, our experience with OpenVINO and TensorFlow Lite for Edge AI on 5G-enabled IoT devices has been insightful. Both frameworks have their strengths and weaknesses, and the choice ultimately depends on the specific use case. By integrating our Edge AI implementation with Intel OpenNESS and Ericsson IoT Accelerator, we were able to leverage their capabilities and create a comprehensive Edge AI solution.

Future Work

As we continue to explore the potential of Edge AI, we plan to investigate other frameworks and platforms, such as Edge ML and AWS IoT Greengrass. We also aim to optimize our Edge AI model for better performance and accuracy.

References

• Intel OpenVINO: https://software.intel.com/en-us/openvino
• TensorFlow Lite: https://www.tensorflow.org/lite
• Intel OpenNESS: https://www.intel.com/content/www/us/en/architecture-and-technology/openness.html
• Ericsson IoT Accelerator: https://www.ericsson.com/en/portfolio/iot/iot-accelerator