When we talk about AI, most of the focus goes to model development, training, and the breakthroughs in large language models or generative systems. But in the real world—where devices must act, respond, and adapt instantly—it’s inference that makes the difference. And increasingly, inference is moving out of the cloud and onto the edge.
Why Inference Matters Now
Inference is what turns AI into action. Whether it’s voice recognition, image classification, anomaly detection, or gesture control, inference enables a device to interpret data and make decisions on the spot. But as more complex neural models become available—especially convolutional and transformer-based networks—traditional CPU or MCU-based systems are struggling to keep up. Performance bottlenecks are leading to sluggish experiences, limited features, or reliance on the cloud for processing, which brings latency, privacy, and cost concerns.
Edge devices need to think faster.
The Case for On-Device Intelligence
Running inference locally—right on the device—has clear advantages:
- Lower latency for real-time responsiveness
- Stronger privacy, since data stays on-device
- Reduced cloud dependence, lowering costs and improving reliability
- Power efficiency, enabling always-on use cases even in constrained environments
This is where dedicated neural processing units (NPUs) come in—purpose-built architectures designed to accelerate AI workloads with far greater efficiency than general-purpose processors. These NPUs tailored for Edge AIare no longer limited to simple, rule-based models or lightweight inference tasks—increasingly, even powerful generative AI models are being deployed directly on edge devices.
Some use cases are emerging where on-device inference delivers transformative value. First, personalized voice assistants powered by LLMs (Large Language Models are enabling context-aware, real-time interactions in wearables, smart appliances, and automotive systems—without sending sensitive data to the cloud. Second, generative vision applications that utilize LVMs (Large Vision Models) are enhancing augmented reality experiences by creating visuals directly on smart glasses or mobile devices, allowing immersive overlays and effects with zero latency.
Market Momentum: AI at the Edge Is Exploding
According to ABI Research Research, NPUs are the fastest-growing segment of embedded AI, with a projected CAGR of 111% through 2030. That surge reflects a broadening demand across sectors—from consumer IoT and automotive to industrial monitoring and smart health.
The growth isn’t just in high-end systems. It’s happening across form factors and price points. That’s why flexibility is key: different devices require different compute footprints and power envelopes.
Ceva’s Scalable Approach to AI Inference
Ceva addresses this diversity with a uniquely scalable and power-efficient AI processing architecture. Our NeuPro family of NPUs is designed to support workloads from the ultra-light to the ultra-demanding.
- NeuPro Nano: Ideal for Embedded ML and always-on sensing applications where power is tight and efficiency is everything.
- NeuPro-M: A high-performance platform that can scale from less than 1 TOPS to hundreds of TOPS (Tera Operations Per Second), supporting complex models in automotive, smart cameras, and industrial systems.
- Big/Little architecture: Ceva offers a unique heterogeneous processing approach—combining high-efficiency and high-performance AI cores in a single design to balance power and performance dynamically.
Whether you’re doing voice wakeword detection in a smartwatch or real-time edestrian detection in a vehicle, Ceva’s NPUs can match the need with right-sized AI.
It’s Not Just Hardware—It’s Enablement
Deploying AI on-device isn’t just about functionality, it’s about how fast and easily you can go from model to product.
Ceva’sunified AI SDK—including model optimization, simulation, and deployment tools—gives developers everything they need to build, tune, optimize, and run AI models on Ceva’s NPUs. Whether working with industry frameworks like TensorFlow or ONNX, or leveraging Ceva’s model zoo, our platform simplifies development while maximizing performance and portability.
This kind of enablement is what helps customers reduce time to market and product risk—turning AI innovation into real-world differentiation.
The Range of Possibilities
With scalable inference and developer-ready platforms, Ceva is enabling next-generation smart edge products across verticals:
- Voice control in low-power IoT devices
- Predictive maintenance in industrial systems
- Computer vision in edge cameras and retail analytics
- Driver monitoring and personalization in automotive
- Gesture and sound classification in wearables and hearables
Each of these use cases requires fast, reliable decision-making—right where the data is generated.
Edge AI That Works. Now.
As the volume of edge devices grows and model complexity increases, localized inference is no longer a “nice to have”—it’s a design requirement.
With Ceva’s NeuPro NPUs, product developers have the tools to meet that requirement—delivering AI performance that’s optimized for the real world at the edge, not just the data center and redefining user experiences.
The Smart Edge doesn’t just think. It thinks fast. And with Ceva inside, it’s ready for what comes next.
Ran Snir