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.

The finite element analysis (FEA) is leading to major breakthroughs in nanotechnology, and having a huge impact on a number of industries spanning electronics, material science, quantum science, engineering, and biotechnology, AZO Nano reported. 

Simulations based on FEA, a complex mathematical technique, is giving engineers valuable insight into the mysterious mechanical properties of polymer nanocomposites used as filler in polymer manufacturing and processing. These properties offer a revolutionary alternative to conventional polymer composites, including enhanced abrasion resistance, less shrinkage, and residual stress, as well as advanced thermal, electrical, and optical properties.  

Nanomaterials are much smaller than traditional materials, and are therefore typically not as effective. As such, it is crucial for engineers to understand how the materials will react under stress in order to improve their design. While FEA is just one technique used to test these designs, its unique abilities provide significant insight into their properties. 

What is FEA?

As we previously reported, FEA is based on the finite element method (FEM), a technique that can help solve highly complex math equations. A simple way to understand FEM is to look at it as separating a large problem into a series of smaller ones (“finite elements”), making the overall problem easier to see. FEA is the mathematical equations behind FEM that is applied to create a simulation. The simulation breaks down the entire model into smaller elements within a mesh, which engineers use to test how the different elements of a design interact and perform under simulated stressors. 

There are many benefits of using FEA. For one, its insight into how the various elements of a design are interacting in minute detail provide enhanced accuracy of structural analysis. Furthermore, FEA allows engineers to create virtual simulations thereby reducing the need for physical prototypes and testing in order to save time and money.

How Are Engineers Using FEA for Advancing Nanotechnology?

Using FEA, researchers have discovered that high interfacial stress can cause the nanofiber or matrix in the material to come apart. They were able to control the properties which improve the strength of the interface to generate the best stress transfer. They discovered that the accumulation of stress concentrations at the interface between the fiber and the matrix can reveal the effective matrix-to-nanofiber stress transfer. Additionally, engineers can use FEA to simulate the composition of nanocomposites and nanotubes in the polymer, which would strengthen their mechanical properties by organizing thousands of nanotubes in a specific pattern.

What Industries Are Benefiting From This Research?

The aerospace sector is using FEA to model and test the effectiveness of polymer nanocomposites-based structures. FEA is also used by the manufacturing sector to simulate the necessary properties of polymer nanocomposites for use in packaging and coating applications.

Engineers are also using FEA to make breakthroughs in the field of photonics. Examples include using FEA to analyze four-wave mixing of topological edge plasmons in graphene metasurfaces; to demonstrate a feasible way to control light on integrated photonics and free-space metasurfaces; and to develop advancements in surface-emitting semiconductor lasers and optical lenses. Learn more about how photonics researchers are using FEA to advance their field.

Problem-Solving Applications with Photonic Devices

Providing a comprehensive and up-to-date account of FEM in photonics devices, with an emphasis on practical, problem-solving applications and real-world examples, Finite Element Method for Photonics is a five course-program from IEEE. Created by Dr. Agrawal, learners will gain an understanding of how mathematical concepts translate to computer code finite element-based methods after completing this program.

Connect with an IEEE Content Specialist today to learn how to get access in order to train your organization.

Interested in the course program for yourself? Visit the IEEE Learning Network (ILN).

Resources

Ahsan, Muhammad Adeel (27 April 2022). Finite Element Analysis of Polymer Nanocomposites. AZO Nano.