There was a time not long ago when adding artificial intelligence to an IoT device was a statement of premium positioning. It meant you were building a high-end product for a niche market. It meant complex engineering, expensive cloud dependencies, and significant trade-offs in power consumption and cost.

That time is over.

We are now witnessing a fundamental shift in the semiconductor landscape. Neural Processing Units, or NPUs, are rapidly transitioning from a differentiator to a baseline requirement. By 2026, edge AI is expected to become the default in many sensors, modules, and gateways. For senior executives evaluating product roadmaps, this is not a trend to watch. It is a decision point.

If your next-generation IoT device does not have dedicated on-chip AI acceleration, it will be at a competitive disadvantage. Here is why.

What Is an NPU and Why Does It Matter Now?

To understand the shift, we must first understand what an NPU actually does. A Central Processing Unit (CPU) is a generalist. It handles operating systems, application logic, and coordination. A Graphics Processing Unit (GPU) is a parallel workhorse, excellent for high-throughput math like video rendering. An NPU, however, is something different.

An NPU is an efficiency engine purpose-built for the matrix and tensor operations that power machine learning models. It is designed from the ground up to handle the specific mathematical patterns of neural networks: convolutions, activations, and pooling operations.

Why does this matter for your business? Because running AI on a general-purpose CPU is like asking a CEO to personally process every invoice. It works, but it is slow, inefficient, and prevents the system from doing what it should be doing.

The architectural implications are straightforward:

• Dedicated hardware blocks execute AI operations with far greater efficiency than repurposed CPU cores
• Optimized memory architectures minimize data movement, which is often the real bottleneck in AI workloads
• Power efficiency enables AI processing on battery-powered devices that would be impossible with CPU-only designs 

The Numbers Tell the Story

The market is moving decisively toward NPU integration. Industry analysts project that by 2026, IoT semiconductors will hit a clear turning point as edge AI shifts from a niche feature to the default across sensors, modules, and gateways.

This is not speculation. It is already visible in the product roadmaps of every major semiconductor player.

Arm, whose processor architectures power the vast majority of IoT devices, has been systematically building out its Ethos NPU product line. The Ethos U55, the world’s first microNPU, was designed for the smallest endpoint devices. The Ethos U65 extended that capability to more powerful systems, delivering twice the on-device machine learning performance while maintaining power efficiency. Most recently, the Ethos U85 has added support for transformer-based models, enabling large language model capabilities on IoT-scale devices.

Intel is embedding NPUs across its Core Ultra platforms, with certain H series SKUs claiming up to 99 total platform TOPS (trillions of operations per second) for diverse inference workloads. AMD has integrated XDNA NPUs into its Ryzen Embedded line, delivering practical on-device AI in 15 to 54 watt thermal envelopes. NVIDIA continues to lead in perception-heavy workloads with its Jetson modules, while Qualcomm has embedded NPUs across its Snapdragon and industrial IoT platforms.

The message from the entire semiconductor industry is unanimous: NPUs are no longer optional.

The Beken Example: NPUs in Mass Market Indian IoT

This brings us to a critical point for the Indian market. NPUs are not just for high-end industrial equipment or premium consumer electronics. They are rapidly becoming accessible for the kind of cost-sensitive, high-volume products that define the Indian smart device landscape.

Our partner Beken has been at the forefront of this democratization. The BK7259 chip, which powers many of Cionlabs’ designs, integrates an Arm Ethos U65 microNPU alongside dual Cortex M55 cores, delivering 0.3 TOPS of AI acceleration. This is not theoretical performance. It enables real-world applications that were previously impossible at this power and cost point.

Consider the implications for products targeting the Indian market:

• Smart door locks can perform fully on device 3D face recognition in under 200 milliseconds, ensuring that biometric data never leaves the device. This is not just a privacy feature; it is a compliance requirement under India’s evolving data protection framework.
• AI toys can respond in real time using natural voice interactions, with the NPU handling audio processing locally while the cloud manages larger language model queries. The result is responsive interaction even when connectivity is inconsistent.
• Battery-powered cameras can run continuous object detection and recognition without draining power. The BK7259 achieves less than 60 microamps keep alive current and 2 microamps deep sleep current, making high-performance AI possible on a single battery.

What makes the Beken approach particularly relevant for the Indian market is its focus on ultra-low power consumption. The BK7259 has set world records for Wi-Fi keep-alive efficiency, drawing less than 50 microamps. For products deployed in environments where battery changes are costly or impractical, this is a game-changer.

The Software Ecosystem: Why NPUs Need More Than Hardware

Here is a nuance that many executives miss. An NPU is useless without a software ecosystem to support it. The hardware acceleration must be accessible to developers, compatible with standard AI frameworks, and supported by tools that simplify deployment.

This is where the Arm ecosystem provides a significant advantage. Beken’s chips support the CMSIS NN software library and TensorFlow Lite Micro, enabling developers to deploy standard neural network models with minimal friction. The Arm Vela compiler further optimizes these models for specific NPU configurations.

At Cionlabs, this means we can take AI models trained in standard frameworks and deploy them efficiently on NPU-enabled hardware. The development cycle is shorter. The performance is predictable. The power consumption is understood from the start.

For senior executives, the software question is as important as the hardware question. A chip with a powerful NPU but poor software support is a liability, not an asset.

Security: The NPU’s Hidden Advantage

There is another dimension to the NPU conversation that is often overlooked: security.

When AI processing happens on a general-purpose CPU, the data being processed often moves through shared memory spaces, creating potential exposure points. When that same AI workload runs on a dedicated NPU within a trusted execution environment, the security posture improves dramatically.

Beken’s implementation achieves PSA Certified Level 2, building on Arm TrustZone technology to create a hardware-isolated secure processing environment. Dedicated components, including a Crypto Accelerator for efficient encryption and a PUF (Physically Unclonable Function) for secure key storage, ensure that sensitive operations remain protected.

For Indian enterprises deploying IoT devices in sensitive environments, this matters. The ability to process biometric data, financial information, or proprietary operational data entirely on the device, without exposing it to the cloud or even to other components on the same chip, is becoming a baseline expectation.

What This Means for Your Product Roadmap

If you are a senior executive responsible for IoT product strategy, the NPU standardization wave has direct implications for your decisions.

First, evaluate your current silicon choices. If your existing products rely on CPU-only processing for AI workloads, you are likely leaving performance and power efficiency on the table. The cost of migrating to NPU-enabled silicon is decreasing, while the competitive gap between NPU and non-NPU devices is widening.

Second, consider your power budget. Many Indian IoT deployments face constraints around battery life, thermal management, and enclosure design. NPUs enable meaningful AI capabilities within these constraints. The BK7259 example shows that always-on, always-connected AI is now possible on battery power.

Third, assess your security requirements. With India’s DPDP Act and evolving cybersecurity frameworks, on-device processing is not just a technical preference; it is often a compliance necessity. NPUs designed with hardware roots of trust and isolated execution environments provide a clear path to compliance.

Fourth, think about future proofing. The NPU ecosystem is evolving rapidly. Arm’s roadmap includes ongoing improvements in power-efficient AI computing. Chips designed today with NPU integration will be able to run more sophisticated models tomorrow, simply through software updates. Chips without NPUs will be left behind.

The Cionlabs Approach: Building for the NPU First Era

At Cionlabs, we have anticipated this shift. Our partnership with Beken gives us early access to NPU-enabled chipsets like the BK7259, along with the software tools and reference designs needed to bring products to market quickly.

When you work with us for white-label solutions or custom product design, you are not getting a generic IoT platform. You are getting a system engineered for the NPU first era:

• Hardware selection informed by a deep understanding of AI workload requirements, power budgets, and thermal constraints
• Software optimization leveraging CMSIS NN, TensorFlow Lite Micro, and the Arm Vela compiler to maximize NPU utilization
• Security integration built on hardware roots of trust and PSA certified architectures
• India is ready to design accounting for our unique operating conditions: temperature extremes, voltage fluctuations, and variable connectivity

Conclusion

The NPU standardization wave is not coming. It is here. By 2026, edge AI will be the default assumption for new IoT devices, not a premium add-on. The semiconductor industry has made its bet. The software ecosystem is aligning. The use cases are multiplying.

For Indian enterprises building smart products, the question is not whether to adopt NPU-enabled designs. The question is how quickly you can migrate your roadmap to take advantage of them.

The devices that succeed in the Indian market will be those that balance intelligence, power efficiency, security, and cost. NPUs are the foundation of that balance. Everything else is a compromise.

Ready to build NPU-enabled products for the Indian market? Let us help you design for the future.

Dr. Sanjay Ahuja is Founder & CEO of Cionlabs, an electronics design house specializing in IoT and AI-enabled hardware. Cionlabs partners with Beken to deliver white-label products and custom designs for smart home, robotics, AI cameras, and industrial IoT applications.