When AI came onto the scene, the cloud was the default destination for innovation. Centralized servers trained massive models, performed complex inference, and powered the applications that reshaped how we live and work. But, as the edge explodes with new devices—from smart wearables to industrial sensors and automotive systems—and these demand real-time intelligence, the limitations of the cloud-first model are becoming clear.
Latency. Privacy. Power. Cost. Personalization. These are the friction points for every company trying to build responsive, intelligent devices for the real world.
And this is where #EdgeAI steps in.
The Edge Isn’t the Future. It’s Already Here.
Today, more data is generated at the edge than ever before. Sensors in factories, cameras in cars, and microphones in smart homes all create streams of information that need to be processed, analyzed, and acted on. In the past, that meant sending it to the cloud and waiting.
Not anymore.
Edge AI moves intelligence closer to the data source. Devices equipped with the ability to perceive, analyze, and respond locally can deliver the real-time performance modern applications demand. They can do it without the latency of a round trip to the cloud. Without the privacy concerns of constant data upload. And without draining the battery or blowing up the bill of materials.
This is the shift. It’s not about replacing cloud computing—it’s about rebalancing the value chain. Inference is moving to the device, while the cloud continues to serve as a platform for model training and big models (e.g. ChatGPT) inferencing. And, as inference workloads grow, so too does the market around them.
The Market Momentum Is Real
According to ABI Research , the intelligent edge is now a $127 billion opportunity and rapidly growing. Neural Processing Units (NPUs) are the fastest-growing segment in embedded AI with a projected CAGR of 111% through 2030. This is because of inference – not training. From predictive maintenance in factories to personalized audio experiences in wearables, real-time, on-device decisions that rely on inference are driving the most growth. In fact, Edge AI inference workloads are expected to outpace cloud-based inference over the next few years, demonstrating that intelligence is moving to the edge where it delivers the greatest value.
What It Takes to Make the Edge Smart
Edge AI isn’t a single capability – it is more of a system-level challenge that spans hardware, software and architecture. Success depends on innovation across multiple layers:
- Connectivity to stream processed data reliably and efficiently between devices and the cloud, across multiple wireless protocols
- Sensing to perceive the environment—through motion, sound, vision, or touch.
- Inference to process and act on data with precision, speed and low power and execute the latest models
- Enablement to bring it all together—through software, developer tools, reference designs and ecosystem integration.
At the same time, AI model development is becoming increasingly commoditized. Tech giants and startups alike are releasing high-performing models delivered as open source. But even while models are now more accessible, they are still a cost item and the real value lies in where and how they are executed.
Today, AI monetization typically happens in the cloud. But, it’s rapidly moving to the edge, where devices need to become connected and smarter. The real differentiator is the ability to run these models on-device, efficiently and at scale. And that’s where embedded NPU IP becomes essential.
Democratizing Intelligence at the Edge
Building edge-capable products from scratch is expensive, time-consuming, and inefficient. Companies looking to differentiate their offerings can’t afford to reinvent the wheel for every core function.
That’s why Ceva’s approach to licensable semiconductor IP and software is so powerful.
With more than 19 billion devices shipped, Ceva provides the essential building blocks that allow OEMs and semiconductor companies to connect, sense, and infer—faster, smarter, and more economically. Our customers gain access to scalable, production-proven IP that helps reduce time-to-market, lower development risk, and accelerate innovation.
Whether it’s TinyML in a wearable, audio intelligence in wireless earbuds, or computer vision in a connected camera, Ceva’s IP is embedded in the devices shaping the next decade of computing.
AI That Works in the Real World
It’s easy to talk about edge AI in theoretical terms. But the impact is real:
- A smartwatch that understands your voice in noisy environments.
- A smart TV that optimizes picture and audio in real time based on room conditions.
- A vehicle that anticipates danger before a human driver can react.
These experiences are made possible by smart edge inference—and by the IP that supports it.
Why Now?
The shift to the edge is driven by both push and pull. Technology companies are pushing for more differentiation in their products, and edge intelligence enables features once limited to flagship devices. At the same time, consumers and enterprises are pulling for faster, safer, and more private experiences. Edge AI delivers all three, enabling a smarter world, one device at a time.
Ceva: Powering the Smart Edge
As intelligence moves outward—from cloud to device—Ceva is the essential enabler. Our IP portfolio spans the core technologies required to connect, sense, and infer data at the edge. And our mission is simple: help the world’s most innovative companies bring intelligent products to life, faster and more efficiently.
The Smart Edge runs on Ceva. And the edge is just getting started.
Iri Trashanski
