Recent advances in edge computing and edge artificial intelligence (AI) are revolutionizing a broad range of industries and enabling a new age in predictive analysis and operational performance—so what exactly is edge AI and how is it changing the way businesses operate?
Edge AI refers to AI computations that are performed near the user at the “edge” of a network and close to where the data is located—which could be a retail store, a workplace, or an actual device such as a phone or a traffic light—rather than long distances away in a central cloud computing facility or private data center. Recent advances in machine learning and high-speed computing, along with the ongoing worldwide adoption of Internet of Things (IoT) devices that continue to deliver faster and more reliable connectivity, have led to the growing deployment of AI models at the edge.
Ultimately, one of the reasons why AI has been so successful when paired with edge computing is because modern-day AI algorithms have become increasingly sensitive to real-world issues and conditions. From the field of healthcare to agriculture and everything in between, AI has become more capable than ever of recognizing patterns and trends within the wide range of different circumstances that are present in real life. As a result, artificial intelligence is highly effective in edge applications and would be far less feasible, and, in some cases, even impossible to deploy in a centralized cloud or private data center. This is due to issues related to latency (delays in network communication), bandwidth (the amount of data that can be transmitted over a network in a specified amount of time), and privacy (the ability to control how personal data is collected, stored, and used).
Because edge technology performs analyses on data locally through decentralized capabilities, it can respond to user needs much quicker while also significantly reducing networking costs for an organization because it requires less internet bandwidth. Furthermore, the processing of data isn’t reliant on internet access, so mission-critical and time-sensitive AI applications can enjoy greater access and reliability. These edge computing benefits combined with the expanding flexibility and “intelligence” of AI neural networks are allowing organizations to capitalize on real-time insights at a lower cost and with greater security and privacy.
Edge AI Use Cases
Edge AI is being recognized as a pivotal technology that will continue to have a major impact on new product development, the streamlining of processes, and the user experience across a broad range of industries.
In the utility industry, for example, edge AI models are combining historical data, weather patterns, and other inputs to more efficiently generate and distribute energy to customers.
In manufacturing, sensor data analyzed by edge AI technology is helping to predict when machines will fail and help factories avoid costly downtime.
Edge AI-enabled surgical tools in the healthcare field are helping doctors make real-time assessments in the operating room that improve surgical outcomes.
In the retail world, edge AI is working to enhance customer service by enabling the convenience of voice-based ordering by customers via smart speakers or other intelligent devices.
In the transportation sector, where real-time decisions can be the difference between life and death, edge AI is being used to adjust traffic lights to regulate traffic flow and reduce congestion.
And in the field of security across numerous organizations, edge AI’s real-time analysis of video footage can identify unwarranted activity and immediately inform authorities.
The Power of Edge AI and Nanotechnology in Semiconductor Applications
According to the authors of Artificial Intelligence in Nanotechnology, an academic white paper on the significant role AI can play in the development of nanotechnology, the incorporation of AI into nanotechnology—defined as the study and control of materials at the nano (molecular, atomic, or subatomic) level to create new, stronger, and more conductive materials and devices—has led to an exciting new vein of research and development called “AI-nanotechnology.”
Thanks to the big data that AI is able to analyze, semiconductors—made up of a wealth of nanoparticles—are immediately benefiting from the combination of edge AI and nanotechnology to design more efficient chips and bring them to market sooner.
Semiconductors, or chips, are components used to conduct or block electric current. They drive a bevy of modern-age devices, including mobile phones, computers, TVs, washing machines, LED bulbs, medical equipment, and more.
The use of edge AI is enabling semiconductor manufacturers to optimize their product’s power, performance, and area (or “PPA,” the three goals of chip design). It benefits PPA by helping engineers to design advanced new chips as well as to efficiently and cheaply overhaul and shrink many older-technology chip designs without needing to update fabrication equipment. By further integrating nanotechnology into this process and being able to design with new and existing materials at nano scales, manufacturers can cost-effectively create more robust semiconductors with improved functionality.
While both of these cutting-edge fields currently face a range of hurdles—ethics, privacy, and bias are issues for artificial intelligence, while nanotechnology struggles with regulatory, environmental, and safety concerns—experts contend that the integration of edge AI and nanotechnology “have the potential to work in concert to spur innovation and solve difficult problems….and [they] hold immense promise for revolutionizing various aspects of science, technology, and everyday life.”
Stay on the Cutting Edge of Continuing Education
A new five-course course program from IEEE, Integrating Edge AI and Advanced Nanotechnology in Semiconductor Applications, explores the intersection of artificial intelligence, edge computing, and nanotechnology through real-life applications and future trends. From the fundamentals of AI nanoinformatics to the specifics of semiconductor design, learners who complete the program will acquire a broad skill set enabling them to navigate the complexities of modern computing.
To learn more about accessing these courses for your organization, contact an IEEE Content Specialist today.
Interested in the course program for yourself? Visit the IEEE Learning Network.
Resources
Yeung, Tiffany. (17 February 2022). What is Edge AI and How Does It Work? NVIDIA.
(16 November 2023). Bringing AI to the Edge: How Edge AI is Revolutionizing Industries. Sintrones.
Agrawal, Radheyshree, Tilak Paras, Devand, Aryan, Bhatnagar, Archana, and Gupta, Piyush. (17 March 2024). Artificial Intelligence in Nanotechnology. Springer Nature.
Nanotechnology. National Geographic.
Brode, Bernie. (21 March 2022). AI and Nanotechnology are Working Together to Solve Real-World Problems. Stack Overflow Blog.
2023 Edge AI Technology Report. Chapter I: Overview of Industries & Application Use Cases. Wevolver.
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