What Is Edge AI?

Edge artificial intelligence (AI) is an artificial intelligence service running on an edge device, such as a smart sensor or other computing device close to the end user. Typically, AI services require massive computing resources and run on cloud infrastructure. Edge AI brings AI capabilities to the network edge, so devices can make decisions in milliseconds without requiring an internet connection.

Bringing edge AI with cost-effective hardware opens a new era of smart device development augmented by machine learning (ML). Edge AI devices can operate more autonomously and securely with no sensitive data transfer and are faster. Users benefit from several aspects of edge AI.

Enhanced data privacy

While secure internet transfer protocols decrease the risk of transferring data via the internet, there is still an inherent cybersecurity risk. When using edge AI, processing occurs on the local device, reducing the possibility of interception. A local approach can enhance cybersecurity and improve data privacy.

Improved efficiency

Transferring data to a centralized cloud computing facility for processing and then waiting for the information to travel back introduces delays. Edge AI reduces system latency by processing data closer to the end user. Your applications transmit lower data volume and use less bandwidth. Edge AI can also manage large data transmission or reception spikes without slowing down. An edge AI model proves much more reliable if your business suffers from regular internet outages.

Scalability

Edge AI allows you to distribute AI workloads across thousands of devices. Scaling up an edge AI system by adding more local devices is easier and more cost-effective, especially in geographically distributed locations.

Technologies like smart monitoring and computer vision provided by edge AI lend themselves to several use cases across various industries.

Manufacturing

You can deploy edge AI within smart sensors in manufacturing plants. Edge AI uses sensor data to monitor part performance and measure overall machine productivity. It can also predict potential equipment failures and mitigate machine downtime.

Similarly, edge AI systems can visually inspect products moving through the plant on conveyor belts. By acting as quality control for products, these systems can identify anything that isn’t up to standard and report it or automatically remove it from the production line.

Retail

In department stores, smart shelves that use edge AI can automatically track stock and generate alerts when inventory levels fall too low. Store workers can more effectively manage floor stock levels and optimize the time spent on inventory management.

Retail stores can also use edge AI security cameras. These cameras can use computer vision and smart monitoring to identify suspicious activity and alert a store worker before an event occurs.

Healthcare

Healthcare providers can use smart monitor technology, enabled by edge AI, to monitor patients’ vital signs. Wearable devices collect information about patient health, helping with the early detection of health issues and more effective patient care. Doctors can also use computer vision to analyze CT scans, X-rays, and other medical imagery to help diagnose, enhancing efficiency.

Agriculture

Farmers can use drone technology and edge AI to closely monitor livestock and crop health. Computer vision and intelligent monitoring enable drones to monitor soil and plant health, detecting potential pests or rot and then selectively applying pesticides or fertilizers. Precision monitoring in agriculture enables businesses to optimize resource usage, reduce costs, and enhance crop yields.

Another use case of edge AI in agriculture is self-driving tractors for harvesting crops. This reduces the manual labor required while increasing farming efficiency.

Edge AI integrates artificial intelligence algorithms directly into local devices, such as smartphones, industrial machines, or IoT devices. These devices have the necessary hardware, such as microcontrollers, processors, and sensors, to collect and analyze data. Edge AI involves running advanced, compact AI models that require less computational power and run efficiently on smaller devices, making immediate data processing possible.

An edge AI solution consists of the following main components.

AI model

An AI model or algorithm is the core of edge AI. A deep neural network (DNN) mimics the structure of human neurons in the brain. Several layers of neurons interconnect and process data simultaneously. Data scientists train these networks to sort information using large data volumes until they produce the correct response. Over time, a DNN can generalize effectively across unknown datasets.

Device management

Manually installing the AI model code on every edge device is challenging. Device management technologies integrate functionalities like code compilation and testing for efficient edge deployment. They convert the AI model into binary code and install it on the edge device. You can automate code testing, security, and deployment so all devices stay up-to-date with the latest features and bug fixes.

Analytics

Once the AI model runs on the device, it collects and processes data as required. Depending on the use case, the edge AI solution might:

• Preprocess and transmit data to another edge device

• Transmit results to an analytics solution

• Take an automated action by itself.

For example, if the device is a temperature sensor, it intelligently observes and sends only critical temperature variations for further processing instead of sending temperature readings every minute. If it is a smart switch, it may automatically regulate temperature as soon as it rises.

People often incorrectly use the terms intelligence edge and edge intelligence as synonyms. While they do have similarities, they are not the same. Edge intelligence makes an IoT device smarter, while intelligent edge is many smart IoT devices working collaboratively to perform a complex task together.

As explained above, edge intelligence is edge AI. It refers to edge AI technology on a single device. It focuses on analyzing large volumes of data and making predictions with ML inference. The final output is transmitted to another cloud or edge device for further processing.

On the other hand, intelligent edge refers to an edge computing environment where many connected devices distribute data processing and other functions across themselves. A network of connected devices, as a whole, manages complex tasks without the need for human involvement. An intelligent edge focuses on how edge technology can move towards distributed architecture. Compute and data storage both occur on the intelligence edge.

Edge AI and cloud AI are both forms of artificial intelligence. The central difference between the two is where data processing occurs.

Data processing

In an edge computing environment, all processing occurs close to the end user on the edge computing device. AI models run on these local devices and process data proximally close to a user to decrease latency, enhance security, and protect sensitive data.

In cloud AI, data processing occurs within cloud data centers. Cloud-based platforms receive and send data from and to a local device. Due to the distance between a data center and the device, this can create some latency problems. AI-powered cloud computing also relies on the internet, which could generate uptime issues if connectivity is essential.

Use case

Edge AI is more useful when low latency and high availability are priorities, such as in a self-driving car that has to make split-second decisions. On the other hand, using AI on a cloud platform is more scalable and offers higher performance and more storage access. Cloud AI may be a better fit for particularly advanced AI models.

While edge AI is a powerful resource, it does have implementation challenges.

Resource limitations

Edge AI algorithms have access to fewer resources than cloud AI. Machine learning algorithms that require more computing and storage resources for complex tasks may be unable to run effectively due to the limitations of edge AI hardware.

Model tuning

As edge devices have limited computing and memory, developers often spend months hand-tuning the artificial intelligence model to achieve acceptable performance within the device's hardware constraints. The additional time it takes to fine-tune a model can create development roadblocks and issues with time to market.

Lack of standardized devices

As edge computing can use a range of local devices, there may be incompatibility between different models. For example, certain camera models may have other hardware, making it more challenging for edge AI developers to create a system that runs effectively on all device types.

AWS offers dozens of IoT services and solutions to bring AI, ML, and IoT together and make your edge devices more intelligent. You can create AI models in the cloud and deploy them to devices with up to 25x better performance and less than 1/10th of the runtime footprint. For example, you can use:

• FreeRTOS, an open-source, cloud-neutral, real-time operating system implemented in over 40 architectures.

• AWS IoT Device Management to register, organize, monitor, and remotely manage IoT devices at scale.

• AWS IoT Analytics to simplify the difficult steps required to analyze massive volumes of IoT data without the cost and complexity of building an IoT analytics platform.

You can also use Amazon SageMaker Neo to optimize machine learning (ML) models for inference on supported devices at the edge. It automatically optimizes machine learning models for inference on edge devices to run faster with no loss in accuracy. With a single click, SageMaker Neo extracts the best available performance for your model on the edge device. You then deploy the model on supported edge devices and make predictions.

For customers looking for pre-made solutions:

• AWS Panorama is a machine learning Appliance and Software Development Kit (SDK) that allows organizations to bring computer vision (CV) to on-premises cameras to make predictions locally with high accuracy and low latency.

• Amazon Monitron is an end-to-end system that uses machine learning (ML) to detect abnormal behavior in industrial machinery. It enables you to implement predictive maintenance and reduce unplanned downtime.

Get started with edge AI on AWS by creating a free account today.