Article
Living on the Edge: AI in Semiconductor Applications
Recent advances in edge computing and edge artificial intelligence (AI) are revolutionizing a broad range of industries. They are 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 Artificial Intelligence
Edge AI refers to AI computations performed near the user at the “edge” of a network and close to where the data is located. This could be a retail store, a workplace, or an actual device such as a phone or a traffic light. It contrasts with processing at long distances away in a central cloud computing facility or private data center. Recent advances in machine learning and high-speed computing have facilitated this change. Additionally, the worldwide adoption of Internet of Things (IoT) devices contributes to faster and more reliable connectivity. As a result, AI models are increasingly deployed at the edge.
Ultimately, AI has been successful when paired with edge computing because modern-day AI algorithms are sensitive to real-world issues. They handle conditions across diverse fields, from healthcare to agriculture. AI is highly effective in edge applications because it recognizes patterns and trends. Deploying it in a centralized cloud or private data center would be less feasible. This is due to issues related to latency, bandwidth, and privacy.
Because edge technology performs analyses on data locally through decentralized capabilities, it can respond to user needs much quicker. It also significantly reduces networking costs for an organization due to requiring less internet bandwidth. Furthermore, data processing isn’t reliant on internet access. Thus, 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. They can do so 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 impact new product development. It will streamline processes and enhance user experience across many industries.
In the utility industry, for example, edge AI models combine historical data, weather patterns, and other inputs. They aim to more efficiently generate and distribute energy to customers.
In manufacturing, sensor data analyzed by edge AI technology is helping predict machine failures. It helps factories avoid costly downtime.
Edge AI-enabled surgical tools in healthcare are assisting doctors. They support real-time assessments in the operating room that improve surgical outcomes.
In retail, edge AI enhances customer service. It enables voice-based ordering by customers via smart speakers or other intelligent devices.
In transportation, where real-time decisions are crucial, edge AI adjusts traffic lights. It helps to regulate traffic flow and reduce congestion.
And in 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 AI in nanotechnology, AI plays a significant role in development at the nano scale. It leads to exciting research and development called “AI-nanotechnology.”
Thanks to the big data that AI analyzes, semiconductors benefit from combining edge AI and nanotechnology. They lead to the design of more efficient chips, speeding up market entry.
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.
Edge AI enables semiconductor manufacturers to optimize their product’s power, performance, and area (or “PPA”). It helps design advanced new chips and cheaply overhauls older designs. This occurs without needing to update fabrication equipment. By integrating nanotechnology, they can design with materials at nano scales. They create robust semiconductors with improved functionality cost-effectively.
While both fields face hurdles—ethics, privacy, and bias for AI, and regulatory issues for nanotechnology—experts believe combining these technologies can spur innovation. 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 program from IEEE, Integrating Edge AI and Advanced Nanotechnology in Semiconductor Applications, explores the intersection of AI, edge computing, and nanotechnology. It covers real-life applications and future trends. From AI nanoinformatics fundamentals to semiconductor design specifics, learners will acquire skills. They’ll be able 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.
Thursday, 5th December 2024