The future of artificial intelligence isn't about choosing between cloud computing and local processing,it's about using both strategically. Ceva, a leading provider of AI chip designs, is being recognized for anticipating this shift, with CEO Amir Panush named "Artificial Intelligence Company CEO of the Year" by the AI Breakthrough Awards program. The recognition reflects a fundamental change in how AI inference, the process of running trained AI models to make predictions or decisions, is being deployed across billions of connected devices.

What Is Hybrid AI Inference, and Why Should You Care?

For years, the AI conversation centered on a simple idea: process everything in the cloud. But that model has limitations. Cloud processing requires constant internet connectivity, introduces latency (delays), consumes significant power, and raises privacy concerns since data travels to distant servers. Panush recognized that the industry was moving toward something different: a hybrid approach where some AI processing happens locally on your device, and other tasks happen in the cloud.

This isn't just theoretical. The practical implications are significant. Imagine a security camera that can detect unusual activity without sending every frame to a remote server, or a factory robot that responds to obstacles in milliseconds rather than waiting for cloud instructions. These scenarios require on-device inference, where the AI model runs directly on the edge device itself.

"AI inference is increasingly a distributed challenge. The cloud will always play a role, but billions of connected devices need to sense, reason and act locally. We built our portfolio to address exactly that need, and the market is now moving squarely in that direction," said Amir Panush, Chief Executive Officer of Ceva.

Amir Panush, Chief Executive Officer of Ceva

How Are Companies Building the Infrastructure for Edge AI?

Ceva's strategy centers on what the company calls its "AI Fabric," an integrated portfolio of silicon and software designs that enable three core capabilities: connectivity, sensing, and inference. This approach recognizes that edge AI isn't just about running a model locally; it's about building a complete system where devices can communicate, understand their environment, and make intelligent decisions in real time.

The company has secured more than a dozen licensing deals for its NeuPro neural processing unit (NPU) IP, which is the specialized chip design that accelerates AI inference on edge devices. These wins span multiple industries and use cases, demonstrating that the hybrid AI model is moving from concept to production.

Meanwhile, other companies are taking complementary approaches. InHand Networks recently launched the Mo 62A and Mo 68A single-board computers specifically designed for edge vision AI applications. These boards come with pre-configured development environments that handle the entire vision AI pipeline: camera input, video processing, on-device inference, and result output. Rather than forcing developers to build the software stack from scratch, these boards provide a foundation that accelerates the journey from algorithm validation to working product prototypes.

Steps to Understand Edge AI Development in Practice

• Algorithm Validation: Developers first test whether their AI model works correctly with sample data, a process that traditionally happens in labs or cloud environments.
• Hardware Integration: The model must then run on actual edge hardware, which requires adapting the software to work with specific processors, cameras, and sensors available on the target device.
• Real-World Testing: Once integrated, the system must handle actual camera feeds, video processing, and inference in real time while managing power consumption and latency constraints.
• Application Development: Finally, developers build the business logic around the AI inference, such as alerts, data logging, or device control based on what the model detects.

Traditionally, steps two through four have been time-consuming and error-prone, requiring extensive lower-level engineering work. The new generation of edge AI platforms aims to compress this timeline by providing pre-integrated hardware and software environments.

What Scale Are We Talking About?

The market opportunity is substantial. Ceva reports that more than 2 billion devices incorporating its technologies ship annually across consumer electronics, automotive, industrial IoT, and mobile markets. Over its entire history, the company has shipped more than 21 billion devices. This scale suggests that edge AI infrastructure is not a niche concern but a foundational layer of the modern device ecosystem.

The diversity of applications is equally telling. Ceva's NeuPro NPU licensing wins span consumer IoT, industrial equipment, automotive systems, infrastructure, and personal computers. InHand's edge AI boards are being used for access control systems, industrial visual inspection, robotics, smart cameras, and autonomous transportation applications. This breadth indicates that on-device inference is becoming a standard requirement across industries, not a specialized feature.

Why Does the Timing Matter Now?

Several factors are converging to make hybrid AI inference practical and necessary. First, AI models have become more efficient. Smaller, optimized models can now run on edge devices without consuming excessive power or requiring massive amounts of memory. Second, specialized hardware like NPUs has matured, providing significant performance improvements compared to running inference on general-purpose processors. Third, privacy regulations and data security concerns are making local processing more attractive to enterprises and consumers alike.

The recognition of Ceva's CEO reflects a broader industry acknowledgment that this shift is real and consequential. Rather than a simple migration from cloud to edge, the future involves intelligent distribution of AI workloads, with each task running where it makes the most sense: locally for speed and privacy, in the cloud for complex reasoning and model updates.

For developers, device manufacturers, and enterprises, this shift means rethinking how AI systems are architected. It's no longer sufficient to build cloud-first solutions and hope they can be adapted to edge devices. Instead, edge AI must be considered from the beginning, with hardware, software, and application logic designed together as an integrated system.