The idea behind edge AI is that it will enable new and more powerful forms of artificial intelligence by combining the power of distributed computing with the ubiquity of mobile connectivity. The result: an AI system that can be deployed anywhere in the world, on any device, at any time, without requiring human intervention. Edge AI runs at the network’s edge, closer to the user. Edge AI is on the rise; according to this study, Edge AI is poised for tremendous growth, it will grow with a CAGR of 25.5% in revenue, and its market size is expected to rise from 346.5 million USD in 2019 TO 1087.7 Million USD in 2024. In this article, we’ll look at how edge AI works, its benefits, and its applications. This is like an Edge AI guide for beginners. Today machines can operate with the intelligence of humans; they can automate repetitive tasks, and make decisions based on the information gathered from the environment, just like humans do. Edge AI takes the Machine learning and analytics capabilities and implements them at the edge for different applications. According to this article on Nvidia.com, three recent innovations are triggering the deployment of AI models at the edge.
- Maturation of Neural Networks: Neural networks can find out relations in a set of data that are non-linear and complex as the human brain does.
- Advances in computing infrastructure: Powerful computational processing is needed on the edge. Thanks to advances in GPUs, it is possible to run such processing at the edge.
- Adoption of IoT devices: The widespread usage of IoT at the edge through sensors, robots, and cameras generates a lot of data that triggers the use of AI models to analyze and take advantage of this data.
Edge AI’s Benefits
There are a few reasons why running AI at the edge is beneficial.
- Edge is closer to the user. Many services are latency sensitive that require processing at the edge closer to the user. It only makes sense to run the AI at the edge near real-time instead of bringing the data to the central location, incurring latencies.
- It reduces the cost of transport. Backhauling data back to the central cloud involves the extra cost of transport bandwidths. This can be avoided by running AI closer to the user at the edge.
3.AI enhances privacy as the data (for example, data related to appearance, voice, or image) is read and analyzed by machines instead of human beings. In addition, the edge further enhances privacy by processing the data locally at the edge and only send only analytical data to the central cloud.
How does Edge AI work?
In a machine learning environment, the AI model is programmed to do specific tasks based on patterns in particular data. This is also called training the AI model. The AI model is then exposed to unseen datasets to test if it can find similar patterns in the unseen data, analyze it, and take specific actions as expected. Fig: Edge AI Model- Ref: embedded computing Now, this kind of AI analysis is quite computationally extensive, and a central cloud that has a lot of cloud resources may seem to be a better location to process the data but sending the data to a central location may come at the cost of latency as well as the use of extensive bandwidth. Furthermore, the growth in the IoT combined with the rise of industry 4.0 and new 5G applications require the data to be processed closer to the user. Therefore, in Edge AI, the AI model is hosted locally in the edge cloud, and thus the processing of the data happens in near real-time at the edge, saving on bandwidth costs and making decisions faster. As edge AI processing is computationally intensive, it requires purpose-built Edge AI processors.
GPU vs. TPU for Edge AI
It would be good to discuss two of the most popular processors used in Edge AI applications: General Purpose Processing Unit ( GPU) GPUs are popular processors for compute-intensive and complex tasks like gaming, video rendering, and 3D graphic. They use parallel processing, i.e., they can break down a difficult task into thousands or millions of sub-tasks and then process them in parallel, making them highly efficient and faster than CPUs. Because of the parallel processing capabilities, they are equally effective as Edge AI applications require processing complex tasks. The well-known GPU makers are Intel, Nvidia, and AMD Tensor Processing Units (TPUs) TPU is an AI accelerator ASIC designed by Google. Google used TPUs internally in 2015 before launching them to the public in 2018. It is worth noting that TPU is designed as a matrix processor instead of a general-purpose processor. With matrix processing capabilities, TPU can process hundreds and thousands of tasks (matrix operation) in a single clock cycle. Regarding AI and machine learning, both GPUs and TPUs are popular. However, TPUs are generally more expensive than GPUs .
Edge AI Use Cases
Industrial Use Cases: Industry 4.0 is a step towards intelligent manufacturing that revolutionaries how an industry manufactures, distributes, and does business. It uses analytics, AI, and machine learning to improve processes, make manufacturing efficient, reduce costs and ultimately make more profits. This would not be possible without running AI on edge. There are many ways in which industries can take advantage of AI. For example, some of them are listed below.
- Predictive Maintenance.
Predicative maintenance in industries is of immense help. The sensors on equipment can report back to the Edge server on the health of the equipment. Based on the data from multiple sensors, the AI/ML model can predict faults early so preventive action can be taken before the actual breakdown
- Quality Control
Multiple surveillance cameras at the production line can scan and send images to the edge server. The AI model is trained to decide and do the quality control report of the finished products in near real-time.
- Autonomous Control
One of the requirements of industry 4.0 is to collect, monitor, and analyze data from different devices and machines and use the data for intelligent and precise decisions to control the machines in real-time so that the assembly lines can be managed autonomously. Edge AI can help in such situations.
Retail Use Cases
- Contactless Checkout
Computer vision aided with AI can help track customers shopping in retail stores. The video analytics can track the items in the cart and help checkout through a mobile app for contactless checkout.
- VR-enabled customer experience
Home improvement stores can provide an interactive experience to their customers by trying the tools and furniture using the power of AR/VR aided with AI.
Health Industry Use Case
Early diagnosis using wearable devices: Wearable devices are becoming popular in the healthcare industry. They provide essential information about patient care, such as blood pressure, blood glucose, and temperature, and report it to the edge server, where the data is processed. Therefore AI/ML models can facilitate doctors by providing reports and early warnings about the patients’ health. Lanner’s Edge AI solutions: Lanner offers integrated and pre-validated Whitebox-based AI solutions with its solutions partners. Lanner’s GPU-accelerated Edge AI appliances are used in broader industrial user cases such as smart cities and industrial 4.0-based smart factory applications EAI-I130 is an industrial Grade AI Inference System with NVIDIA® Jetson NX used for 5G edge applications. IIoT-I530 with NVIDIA® Jetson NX is AI Inference System For 5G Edge LEC-2290E is an Nvidia NGC-ready Edge AI device with NVIDIA® T4 GPU Compatibility Lanner’s white box solutions Lanner Electronics is a leading manufacturer of white box and uCPE platforms. It provides compact white box solutions and uCPEs for a wide range of applications like Edge cloud/MEC, Open RAN, NFV, SDN, SD-WAN, network orchestration, and network slicing. Lanner operates in the US through its subsidiaries Lanner Electronics USA, Inc. and Whitebox Solutions (whiteboxsolution.com).