If you’ve heard of the “Midas touch,” you’ll know exactly what we’re referring to when we say that AI (Artificial Intelligence) is transforming industries like never before. It is fueling the development of applications that seemed virtually impossible until recent times.
The growth of AI and ML (machine learning) has also led to the exponential growth of data and real-time responses. As a result, the focus on edge AI is only increasing.
Why is edge AI important? Essentially, it helps in the proper implementation of distributed systems, where data can be processed right from the point of its origination. This means minimal delays, and greater privacy and security.
Edge AI has a lot of promise to change how embedded systems handle processing and workloads. In fact, AI is gradually moving from the cloud to the edge of the network.
Is edge directly dependent on AI for proper functioning? This is like getting into a Sitecore vs WordPress debate, where it can be concluded that: it depends on your content management and website performance needs.
Curious to know more about edge AI? Let’s begin by first understanding what edge computing is.
What Is Edge Computing?
Edge computing has been defined as “a distributed information technology (IT) architecture in which client data is processed at the periphery of the network, as close to the originating source as possible.”
This means that instead of transmitting raw data to a centralized data center for analysis, it can be processed right from where it is generated. This could be a retail store, a factory floor, a warehouse, or even a smart city.
Meanwhile, the edge comprises all computational sources at or below the cell tower data center. It can also include on-premises data centers.
There are three types of edges that have some commonly associated equipment. These are:
- Thick edge: Located within a data center, these computing resources are equipped with components that can manage computing tasks, such as data storage and analysis. Examples include high-end CPUs (central processing units) or GPUs (graphic processing units).
- Thin edge: These refer to intelligent computers, networking systems, and controllers that gather data from sensors and other devices that generate data.
- Micro edge: These are the intelligent sensors and any other devices that produce data.
In this context, edge AI is the deployment of AI models on a device or equipment at the edge. This enables AI-powered decision-making and AI inference without the need for continuous cloud connectivity.
However, this is not to be confused with AI Copilot or AI Search, which may or may not rely on the cloud for their proper deployment.
Four Trends in Edge Computing to Look Forward to in 2025
Mentioned ahead are some of the key trends that are set to reshape edge computing in the near future.
1. NVIDIA Will Gain Prominence
This US chipmaker is a pioneer in driving the adoption and implementation of AI across industries. It released the Tesla GPU back in 2007, marking a shift towards high-performance computing. Since then, it has developed its own GPU, which is globally acclaimed for its superior performance, specifically in data centers. NVIDIA’s GPUs now play a big role in deploying complex AI models at the edge.
Ever since NVIDIA found out that GPUs can be used for parallel processing other than graphics, it has increased its focus on Artificial Intelligence computing. It created the NVIDIA CUDA Toolkit and empowered GPUs to perform compute-intensive tasks.
Today, NVIDIA is a leader in its industry, thanks to its powerful GPU architecture, sophisticated AI research and development capabilities, focus on AI training and inference, and future-centric innovations. It has a robust ecosystem of AI-specific hardware and software—all of which will continue to push it to the top of the AI and Embedded Systems market.
2. Development of “TinyML”
This term refers to the evolution of machine learning models that are tiny enough to be deployed on edge devices with fewer computational resources, such as sensors, microcontrollers, smartphones, and so on. TinyML aims to optimize AI algorithms so they can work efficiently on hardware with minimum memory, processing power, and energy consumption.
Smaller models are trained to emulate larger, complex models by reducing the precision of numbers used in model computations. For instance, 8-bit integers are used instead of 32-bit floating-point numbers. This can also be done by removing unnecessary neurons or connections in a neural network.
The architecture of TinyML is such that it balances performance and efficiency. Hence, AI models can run on devices with very limited computational resources. SqueezeNet is the most common example of a smaller neural network architecture that enables high performance on high devices.
Going forward, the integration of TinyML with edge AI is all set to bring about exponential growth in areas like latency improvement, privacy, bandwidth, and energy efficiency.
3. Increasing Role of Cellular IoT
Still in its nascent stages, the integration of AI and cellular IoT (Internet of Things) holds significant potential to transform industries. This convergence means AI inference can take place at the edge, enabling quick and informed decision-making. As a result, less data is transmitted over cellular networks, bandwidth and costs are saved, and autonomous decisions can be made in minutes.
Moreover, embedding AI chipsets within connectivity modules can save space and organize the form factor of IoT devices. This goes beyond data communication where intelligent edge nodes can manage workloads independently.
While these modules are already being applied in smart cities, healthcare monitoring, and industrial automation, their application is only meant to grow. In the future, these modules will become capable of maintaining a continual data flow without disruptions, even in remote and mobile environments.
4. NPU Integration
NPU (Neural Processing Unit) is a hardware accelerator that aids faster data processing, lowers latency, and reduces power consumption. Applications of NPU include optimizing the performance of AI and ML tasks, particularly those related to deep learning.
Unlike CPUs and GPUs, NPUs are used specifically for performing advanced mathematical tasks, such as convolutions and matrix multiplications, which are required for neural networks. They’re also used to develop smaller AI systems, which can be embedded into other devices.
Used mainly in the development of smartphones, wearable tech, and IoT sensors, NPUs result in compact hardware, reducing the need for physical space. Because they’re a part of custom System-on-chip (SoC) designs, multiple components can be integrated into a single chip. Further, NPUs are energy-efficient and, therefore, instrumental in improving the longevity of battery-powered devices.
Moving forward, NPU will find greater application in features associated with augmented reality, virtual reality, voice recognition, and on-device facial recognition.
Conclusion
The fusion of AI and Embedded Systems is changing industries for the better. Systems are becoming increasingly capable of managing heavy workloads without connectivity issues. As mentioned above, several key trends are ready to emerge in the coming year.
It is clear that the new generation of Embedded Systems has borrowed greatly from the development of ML and deep learning algorithms to create the smart devices we know and use today. As we look ahead, these devices are expected to become smarter, faster, and more efficient in every way possible.
