People spend most of their time on mobile gadgets and different electronic devices in today's era. Organizations and developers understand the importance of deploying AI at the edge of devices to provide efficient and immediate services to their customers and increase their revenue. Edge AI platforms help bring computation and data storage closer to the devices where it's being gathered, eliminating the need to rely on a central location. When we talk about real-time data processing, users won’t face latency, bandwidth, or security issues that can affect an application’s performance. Edge computing and AI are growing exponentially due to the increase in IoT devices, making on-device analytics more crucial for smart manufacturing and autonomous vehicles. Edge AI applications offer vast potential in smart cities by enabling edge artificial intelligence for local decision-making and data privacy in AI-driven solutions.
Edge AI architecture is very beneficial in manufacturing, surveillance, and monitoring industries. Read more about Edge AI Applications and How do they work.
Edge AI states that running the machine learning algorithm locally on a hardware device using edge computing and AI algorithms is based on the data created on the device without requiring any connection. For example, someone may ask Siri to call a particular person, ask Alexa to play a specific song, or ask for a roadmap for a particular location on Google Maps. This allows users to process data with the device in less than 400 milliseconds, providing real-time data processing. Through Edge AI, the user communicates with applications like Google Alexa and Apple Siri by sending voice recordings to an edge network. The voice recording is processed by passing text via AI-driven solutions.
What is Edge AI Architecture?
Here are the three layers on which Edge AI is constituted:
IoT layer
The IoT layer is embedded in mobile devices, smart cars, smart fridges, sensors, actuators, and controllers to monitor objects in services, human activities, or operations. It uses wireless standards like WiFi to connect devices and facilitate AI-driven solutions for real-time insights.
Edge layer
The Edge layer serves as the central component of the Edge AI architecture, where edge AI computing and edge intelligence come into play. It analyzes data, manages computing tasks, orchestrates policy schedules, and helps monitor and update technological resources to manage organizational activities. This layer processes and filters data generated through the IoT layer in real-time, ensuring data privacy and on-device analytics, which are then sent to the next layer for further business intelligence applications.
Business Solution Layer
The Business solution layer consists of business applications, authentication, and a set of services. It incorporates machine learning at the edge, AI frameworks for edge computing and data analytics to provide complex functionalities. This layer handles workloads that require high-level processing, including AI and edge computing tasks. It is responsible for visualizing data, deploying AI-driven solutions, and facilitating AI democratization for enhanced decision-making.
Components of the Edge AI Stack
The Edge AI stack encompasses various components that organizations need for the right implementation of edge AI. Below are the key layers of the stack:
Custom Design Services
The ecosystem of design services facilitates the development of broad market applications, including smart cities, smart cars, and IoT devices. These services are essential for creating AI-driven solutions that operate effectively at the edge.
Reference Designs/Demos
We leverage edge AI applications like face detection to unlock mobile phones and other use cases, including speed breaker detection, voice recording, and object counting. These reference designs showcase how AI at the edge enhances device functionality and improves user experiences.
Software Tools
This layer defines the software tools used for tasks like face detection or voice recording. Examples include Neural network compilers for frameworks like Caffe and TensorFlow, which are optimized for edge artificial intelligence applications on platforms like FPGA.
IP Cores
IP cores are crucial for accelerating edge AI applications. These include components like Convolutional Neural Network (CNN) accelerators and Binarized Neural Network (BNN) accelerators, which enhance performance in edge computing tasks like face detection, voice recording, and keyphrase detection.
Modular Hardware Platforms
Popular hardware platforms in the Edge AI architecture include the award-winning Embedded Vision Development Kit and the iCE40 UltraPlus device-based Mobile Development Platform (MDP). These platforms describe the edge computing hardware embedded in devices, enabling them to handle machine learning at the edge effectively.
Explainable AI in manufacturing improves efficiency, workplace safety, and customer satisfaction by automating their tasks. Click to explore our, Explainable AI in Manufacturing Industry
Use case of Edge AI
Here are major use cases for Edge AI:
-
Surveillance and Monitoring Purposes: Before Edge AI, the output created by security cameras was transferred to the cloud, containing raw video signals continuously streamed to the cloud server. The large volume of video footage moved to the cloud caused a heavy server load.Using edge AI, machine learning-enabled smart cameras can locally process captured images to spot and track multiple objects and other people and detect suspicious activities directly on edge. Camera footage does not transfer to the cloud server, reducing bandwidth, latency, and security issues. Now, servers can easily communicate with many cameras to minimize remote processing and memory requirements.
-
Smart Devices: Nowadays, almost everyone is familiar with face detection and face tracking, such as Google Home, Alexa, and Apple Siri, and they all use Edge AI. In this, words like Wake, To-Do list, and phrases such as "Alexa" have already been trained with a Machine Learning Model and processed locally on the speaker. Whenever it hears the word "wake," it will be sent over the internet to the Amazon Alexa voice service that helps phrase voice into commands it understands. After processing, it will show you the desired output.
-
Autonomous Vehicles: In autonomous vehicles, edge computing allows for immediate data processing within the vehicle itself, eliminating the need to send data to a cloud server. With edge AI, crucial information such as recognizing vehicles, traffic signs, pedestrians, and roads is processed instantly, ensuring the vehicle operates safely. This real-time data processing is essential for making split-second decisions, enhancing the safety and autonomy of vehicles.
-
Healthcare: In the healthcare sector, edge AI facilitates autonomous monitoring of hospital rooms, detection of cardiovascular abnormalities, fractures, and musculoskeletal injuries, as well as diagnosing neurological diseases. By processing data locally, edge AI computing allows doctors to make faster decisions during emergencies, improving patient care and satisfaction. It also helps hospitals stay competitive by leveraging AI-driven solutions for better patient outcomes.
-
Industrial IoT (IIoT): The future of manufacturing is being shaped by Edge AI, where factories will be more efficient with automated inspections and robotic control for visual assembly. By deploying edge artificial intelligence, manufacturers can develop AI capabilities at a low cost, process data quickly, and improve production efficiency. This is especially important in smart manufacturing and industrial IoT environments where fast, real-time decision-making is crucial.
Implementing Edge AI in Edge Computing is because of its flexibility and enabling smart devices to support different industries. Explore here about Bringing AI at the Edge
The Future Landscape of Edge AI Technology
-
Cost Reduction and Enhanced User Experience: Edge AI is transforming industries by reducing costs and latency times, thereby improving the user experience. Organizations are increasingly recognizing the value of Edge AI platforms and integrating this technology into their devices to offer users faster and more efficient services. This will help companies attract more customers and boost demand for their products in the market.
-
Improved Data Security: One of the key benefits of Edge AI is enhanced data security. By processing data locally on edge computing devices, there is no need to transfer sensitive information to a cloud server, reducing the risks of data breaches and enhancing data privacy in AI.
- Reduced Bandwidth Requirements: Moreover, since data no longer needs to be sent to the cloud, bandwidth requirements are reduced, which can lead to lower internet service costs, making edge computing more cost-effective for organizations.
-
Growth of Autonomous Technology: The demand for autonomous technology is growing rapidly. With edge computing and AI, devices can operate independently without the need for continuous support from data scientists or AI developers. This makes Edge AI applications more scalable and accessible, supporting industries in areas like autonomous vehicles, smart cities, and industrial IoT.
Harnessing Real-Time Capabilities with Edge AI
Edge AI offers almost endless possibilities, particularly in the realm of IoT devices. By leveraging edge AI, real-time operations become possible, enabling immediate data creation, decision-making, and action where milliseconds matter. This is crucial in areas like autonomous vehicles, robots, and numerous other fields. Edge computing AI ensures that data is processed locally, reducing latency and allowing for faster, more efficient responses in time-sensitive scenarios.
More Ways to Explore Us
Edge AI for Real-Time Object Detection
Edge AI For Autonomous Operations
Robustness and Reliability in Edge AI Systems
