Edge AI (Artificial Intelligence): Optimizing Data Processing

Copperpod IP
9 min readMay 24


The field of Edge AI is rapidly expanding as an emerging technology. According to the LF Edge group, the power consumption for Edge devices is predicted to increase from 1 GW in 2019 to 40 GW by 2028, with a compound growth rate of 40%. Also, mobile and residential consumer use cases will account for 35–40% of global infrastructure Edge demand and Edge AI is expected to grow alongside this trend.

Edge AI (Artificial Intelligence): Optimizing Data Processing

What is Edge AI (Artificial Intelligence)?

Edge AI is a combination of Edge computing and artificial intelligence. The term “Edge AI” is coined as a method in which AI processing is carried out at the user end, near the edge of the network where the data is situated, instead of being centralized in a private data center or a cloud computing facility. More specifically, Edge AI refers to a method of processing data using machine learning algorithms on a hardware device at the local level, without requiring an internet connection. Edge AI brings data and its processing closer to the user by leveraging the computing power of local devices such as computers, IoT devices, or Edge servers. This allows for real-time decision-making in mere milliseconds and significantly reduces communication costs associated with cloud-based models.

One example of Edge AI technology in action is found in smart speakers like Google, Alexa, or Apple HomePod, which utilize machine learning to learn words and phrases and store them locally on the device. When a user communicates with applications like Siri or Google Assistant, their voice recording is sent to an Edge network where it is converted to text using AI and a response is generated in under a few milliseconds, without Edge technology, response times would take seconds.

How Does Edge AI Work?

In order to perform tasks such as object detection, driving cars, understanding speech, speaking, walking, or imitating human abilities, machines must replicate human intelligence in a functional manner. One way in which artificial intelligence achieves this is by using deep neural networks, which are structured to mimic human cognition. These networks are trained to provide specific types of answers by being exposed to numerous examples of those types of questions along with the correct responses. This process of training, known as “deep learning,” typically takes place in a data center or the cloud due to the vast amount of data necessary to create an accurate model. Also, the training process requires the collaboration of data scientists in configuring the model. Once trained, the model becomes an “inference engine” and is capable of providing answers to real-world queries.

For instance, when a face recognition system captures the face of a person, it sends the captured face data to a cloud server, where the inference engine recognizes the person and sends back a response to the face recognition system. Whereas, in Edge AI implementations, the inference engine operates on a computer or device in remote locations, including factories, hospitals, cars, satellites, and homes. Thus, Edge AI is able to provide a response in a few milliseconds with increased cyber security.

If the AI encounters a difficulty, the problematic data is typically transmitted to the cloud for additional training of the primary AI model, which eventually replaces the Edge’s inference engine. This feedback loop is a crucial element in improving model performance; once Edge AI models are put into operation, they continually enhance their capabilities.

Benefits of Edge AI

Edge AI delivers a range of benefits, including faster decision-making, heightened data security, improved user experiences through hyper-personalization, and lower costs.

  • Latency

The process of transferring data to and from the cloud is associated with a certain amount of latency, usually around 100 milliseconds. While this may not be a significant issue in many cases, there are instances where even this level of latency is too high. Take for example the case of new Porsches, which are equipped with hundreds of sensors that constantly generate large amounts of data about the car’s performance. To address this, Porsches are integrated with NVIDIA’s GPU processor and Kinetica’s analytics software, which allow for automated responses, including taking control of the vehicle if necessary. However, when the car is traveling at high speeds, such as 200 kilometers per hour, even a delay of a few milliseconds can have serious consequences. If the decision to brake is made too late, the car may end up in a ditch.

  • Real-time Analytics

Edge computing enables almost real-time analytics, with analyses being conducted in mere fractions of a second, which is particularly important in time-sensitive situations. Consider, for instance, a machine (robot) on a factory assembly line. If a robot on the assembly line is activated at an incorrect time or is delayed, it could lead to a defective product or cause the product to continue moving down the assembly line without being processed or touched. If this error goes undetected, the faulty product may be released into the market or cause damage in subsequent stages of production.

  • Scalability

According to International Data Corporation (IDC), by 2025, there will be 55.9 billion interconnected IoT devices generating a staggering 79.4 zettabytes of data. As data volumes continue to rise, there is a growing need for innovative and efficient methods of analysis and processing. Edge computing plays a crucial role in this regard, as it allows for local data processing, reducing the risk of centralized services or data transfers becoming a bottleneck. This is particularly important in Edge AI use cases that involve vast amounts of data, such as processing video image data from hundreds or thousands of sources simultaneously. In such scenarios, transferring data to a cloud service is often not a practical or viable solution.

  • Information Security and Privacy

By performing data processing on Edge, there is a reduced risk of online attacks since less data is stored in the cloud. Also, the closed network within which Edge devices typically operate makes stealing information a more challenging task, while the presence of multiple devices within a network makes it more difficult to bring down.

In general, any system that involves security considerations should be executed on Edge. For instance, consider intelligent safety monitoring systems in a factory setting, where it is crucial for alarms to be triggered before an accident happens, such as when machines malfunction and when data processing is conducted locally, data does not need to be transmitted to a cloud environment, thereby making it more challenging to access the data without authorization. Real-time sensitive data, like video data, may only exist for a brief period before disappearing, which makes it easier to ensure data privacy and security since intruders would need to gain direct access to the physical device where the data is being processed.

  • Automated Decision Making

Edge AI enables the devices to operate on their own and allows them to take decisions based on the situation within a short span of time. For example, a self-driving car is equipped with numerous sensors that continuously measure various parameters such as the vehicle’s position and the speed of tire rotation. The driving computer can then automatically make decisions regarding steering, braking, and throttle usage based on the collected sensor data.

  • Reduced Cost

Cloud-based data processing and analysis can be costly. Edge computing can significantly reduce costs of cloud capacity such as GPU computing for organizations where a swift response is required for analyzing large historical data or constant data streams. Also, Edge computing can conserve bandwidth, as it minimizes the need for data transfer, and leads to an increase in the energy efficiency of the devices.

Applications of Edge AI

As a relatively nascent technology, Edge AI holds immense promise, and we can expect to see an exponential rise of new use cases emerging in the near future. Some application areas are:

  • Healthcare

Edge devices can play a pivotal role in AI healthcare applications, including remote diagnostics and surgeries, and monitoring of vital signs of patients. By processing AI algorithms at Edge, doctors can safely operate surgical tools from a remote platform, providing more comfort and security.

  • Smart Homes

Smart homes are equipped with a variety of IoT devices that work in conjunction to simplify the lives of their inhabitants, from voice-controlled light bulbs and video doorbells to refrigerators that monitor food consumption and expiration dates. Rather than transmitting all of this data to a remote server for processing, Edge AI allows for on-site processing, resulting in faster and more secure operations.

  • Autonomous vehicles

The quick and efficient processing of data is crucial for the safe and reliable operation of autonomous vehicles, especially when navigating busy roads. Edge AI technology allows for rapid data processing, which enables the vehicle’s system to respond quickly to its surroundings, ultimately improving its safety and reliability.

  • Industrial IoT

Smart factories can utilize Edge AI to enable the remote operation of heavy machines, particularly those located in hard-to-reach and unsafe places, with the aim of improving safety and productivity. Also, it can provide a safe and comfortable working environment.

Revolutionizing Inventory Management through Edge AI

Maintaining a physical count of items present on a store’s shelf is a tedious and time-consuming task. The count of different items in the inventory is maintained in a database. Further, there is a frequent need to compare the count of items stored in the inventory with the physical counts of the objects in the store. This process of cross-checking the physical inventory with the logical inventory stored in a database is manual and time-consuming.

To overcome this issue there are some existing tools and solutions. The existing tools/solutions automate the counting of items of different types. For example, there are mobile applications that can scan tags and labels on items and store the information in an inventory system. However, this approach requires the manual movement of a person and is time-consuming and costly. Also, there are some other approaches that require drones, satellites, and robots with a camera system. However, the major difficulty with these approaches is the obstructed view of camera-based systems. For example, in a grocery store where items are placed behind one another on a shelf, a robot with a camera may only capture an image of the first item, leaving the remaining items out of sight, therefore, making it impossible to automate item counting.

Seminal Patent

Title: Apparatus for automating inventory and automatic inventory system and method

Patent Assignee: International Business Machines Corp (IBM)

The Patent application discloses a novel apparatus/ method to address the inventory management challenges in shops and warehouses. The disclosed apparatus comprises a mobile mechanical device with a processing system and a movable arm. In detail, the arm includes a camera and an additional sensor such as a microphone, touch sensor, and weight sensor. Further, the processing system comprises a positioning system for moving the arm to images of the items placed on the shelves from different perspectives, a context module to determine the context of the device, and an Edge AI computing module.

For example, the mobile mechanical device moves to a shelf in a store/ warehouse in which multiple items are placed. The positioning system configures the movable arm to capture images of the items on the shelf and collects additional information using the sensor. Further, the Edge AI computing system utilizes these captured images and the additional data to identify the item and create an AI-based counting model. These AI-based counting models are product specific and are used for counting the items.

Future Scope

The field of Edge AI is rapidly expanding as an emerging technology. According to the LF Edge group, the power consumption for Edge devices is predicted to increase from 1 GW in 2019 to 40 GW by 2028, with a compound growth rate of 40%. Also, mobile and residential consumer use cases will account for 35–40% of global infrastructure Edge demand and Edge AI is expected to grow alongside this trend. Currently, the majority of Edge AI use cases are found in consumer devices such as smartphones, wearables, and smart appliances. However, enterprise Edge AI is expected to grow at a faster rate, particularly with the emergence of technologies such as cashier-less checkout, intelligent hospitals, smart cities, Industry 4.0, and supply chain automation.

As digital services become more advanced and require distributed computing resources, Edge computing is predicted to experience significant growth in the coming years. Edge computing presents a promising market, and although it may encounter obstacles, its foundation is built on solid ground, supported by convincing use cases that offer compelling value propositions.




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