Why is Edge AI Required?
Often, models are deployed in remote locations with little to no internet connectivity. In such cases, getting inferences from the models becomes impossible if they are deployed on the cloud. In case the data required for inference is in large quantities, uploading the data to the cloud and waiting for inference becomes cumbersome and slow.
A nuclear plant located in a remote location would require regular sensor data monitoring to check for anomalies or radiation leakage. In such a situation, using Edge architecture would serve better than the cloud as it provides instant inference, and there is no need to upload the sensor data to the cloud each second.
Edge AI Devices
Commonly known devices used for Edge AI and Computing:
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Raspberry pi
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Lenovo ThinkEdge
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Advantech IPC-200
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Google Coral boards
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Jetson Series (NVIDIA)
Architecture: Most edge devices like Raspberry Pi come with a 64-bit processor and RAM. Since edge devices are meant to be lightweight in processing, the memory comprises 1-4GB RAM. Still, some edge devices can upgrade RAM to higher memory to accommodate models that require high face detection model processing power. There are slots for SD-card storage and HDMI ports for input and output. Edge devices also come with a port for power supply and ethernet connectivity.
It is essential to mention that input and output devices, such as cameras or display screens, are designed specifically to work with edge devices. For example, Pi Camera is designed to work with Raspberry Pi to capture high-definition images and videos.
Edge AI Platforms
Well-known platforms for Edge AI and computing:
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AWS Greengrass: AWS Greengrass is an open-source platform for managing IoT edge devices. It provides services for building, deploying, and managing edge device models. The Greengrass software is deployed on edge devices connected to Greengrass cloud services for support.
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Azure IoT Edge: Azure's IoT Edge service provides a cloud platform for managing edge devices and using Azure's services and packages on them.
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Google Distributed Cloud Edge: Google's distributed cloud edge provides Google cloud services on edge devices. It is fully managed by Google, which also provides hardware solutions. It offers real-time data analytics with Google AI and analytics.
Edge computing deploy the services at the edge seamlessly and reliably by maintaining flexibility. Taken From Article, Applications of Edge Computing
Applications of Edge AI in the Industry
The Internet of Things (IoT) is a system of interconnected devices working on Edge architecture. Apple Inc.'s Siri is an AI voice assistance application that does not need internet connectivity to operate. Similarly, many computer vision applications are gradually moving towards Edge architecture to deploy their models.
NVIDIA Metropolis is an application framework for creating and deploying Edge automation and AI applications to increase the efficiency of metropolitical institutions like airports, factories, farms, hospitals, etc. Arizona's Maricopa County Department of Transportation (MCDOT) has used NoTraffic, an NVIDIA Metropolis partner, to reduce traffic on roadways in Arizona by using deep neural networks and computer vision to track real-time traffic flow.
Some sectors in which Edge AI is applied:
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Computer Vision: Surveillance systems utilize Edge AI for object detection, face recognition, and tracking to identify anomalous behavior, unauthorized access to systems or areas, identifying subjects with past criminal records, etc., to safeguard the organization and locality. Instant detection and recognition can aid security personnel in taking immediate action and stopping the malicious attack before it can cause further damage.
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Manufacturing: Data streams from manufacturing machines can be used for real-time analysis using Edge AI models to monitor the manufacturing process, control temperature/pressure conditions, optimize raw materials, etc. AI models can predict faults in the machinery by continuous analysis of the sensor data stream, which can lead to timely maintenance and calibration of the machinery, thus increasing productivity and reducing damage control requirements.
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Self-driving cars: For self-driving cars to work properly, constant sensor data input and frequent analysis of the input data at millisecond frequency are needed. Edge AI provides the best infrastructure for instant inference of the sensor data to guide the car's controls. Since the architecture is self-reliant, low bandwidth will not cause any problems.
Cloud-Edge trade-off
Deploying AI models on the cloud or Edge is often a confusing decision. With both technologies having pros and cons, the enterprise decides which features are more critical for them. If the enterprise requires instant inference from the model on real-time data or the site of operations is in a remote location with no or sparse internet connectivity, edge AI is a better choice. Edge devices provide additional security and privacy since the data is not transmitted to cloud servers and is kept on-site.
Sending data to cloud platforms requires bandwidth and storage, but edge processing reduces that cost. Suppose the enterprise can afford high latency and does not require regular model inferences. In that case, cloud infrastructure is better since it is much simpler and does not require hardware maintenance and configuration. However, using Edge or Cloud architecture is usually not a two-way street since Edge architectures use cloud platforms to maintain their Edge nodes and update their models.
Conclusion
Edge AI is fairly new in computation but has grown exponentially. Each year we see new applications and new technologies developing around it. Edge AI architecture is very beneficial in manufacturing, surveillance, and monitoring industries. With instant inference deliverance, little to no internet connectivity need, data security, and privacy, as well as cost efficiency, Edge AI has the potential to revolutionize how AI technology is developed and used worldwide. With its convenient architecture, Edge technology can help AI implementation grow and be used more widely by the masses and institutions.
- Discover here about Role of Edge AI in the Automotive Industry
- Read about Edge AI in Video Analytics