By Cosmin Balan

CES 2026 marked a clear transition for the tech industry: intelligence is no longer confined to screens and servers; it’s moving into the physical world. This year’s stars of the show were robots and AI-driven devices that sense, interpret, and act in real environments, pointing toward a future where ambient and physical AI become foundational to next-generation products. The industry focus has shifted towards devices that can interact and adapt to the real world. 

This shift has profound architectural implications. As devices increasingly need to perceive and respond locally, with minimal latency, power draw, and reliance on network connectivity, traditional computing models fall short. Neuromorphic computing architectures, inspired by how biological brains process information, are uniquely positioned to meet these needs by offering real-time, event-driven processing with extreme energy efficiency. So, what are the three CES 2026 trends where neuromorphic matters most?  

1. Robots That Perceive and Act in the Physical World

Robotics was hands-down the leading theme at CES 2026. From household helpers to industrial partners. Hyundai Motor Group unveiled a broad AI robotics strategy that brought humanoid robots like Boston Dynamics’ new Atlas out of the lab and onto the CES stage, signaling a push toward the commercialization of adaptive, task-capable machines. Meanwhile, companies like MyMemo AI debuted humanoid home robots designed to interact with families and integrate with smart home environments.

These systems must fuse inputs from cameras, tactile sensors, motion detectors, and more – and decide how to act in milliseconds. Traditional compute architectures can struggle here due to high power and latency costs. Neuromorphic architectures excel in event-driven, parallel processing of sensory data, enabling faster, local and more efficient perception and control loops that robots require. 

When considering the mass-market deployment of robotics, power and cost challenges need to be addressed. This is where solving the data at the edge challenge with neuromorphic chips, such as the ready-to-deploy Innatera Pulsar, becomes critical. In other words, using the right intelligence at the sensor edge allows for efficient insight extraction, ultra-low-power local processing, and a central computing system that is uniquely efficient in power and cost.

2. Smart & Autonomous Home Devices

Beyond robotics, the event showcased a new wave of home automation and consumer devices that integrate sensing with context-aware logic. Roborock’s Saros Rover, capable of climbing stairs and navigating cluttered spaces autonomously, shows how AI can transform even routine household chores with smart perception. LG’s CLOiD home robot demonstrated an early but meaningful step toward hands-on assistance within the home.

At the same time, smart locks like Aqara’s U400 introduced enhanced presence detection and interoperability standards relying on UWB and local context inference. These trends underscore that smart homes are evolving from connected to contextually intelligent. Devices increasingly need to interpret who is present, what they’re doing, and how to respond.

Neuromorphic architectures are particularly well-suited for this “smarter home without the cloud” vision. Rather than constantly relaying raw sensor streams to a cloud service, neuromorphic processors can detect events of interest and classify them locally with minimal power usage, marking this architecture critical in scenarios where responsiveness and privacy matter. 

3. Wearables & Physical AI Agents

Lastly, CES also highlighted AI now being embedded in wearables and personal assistants. Concepts like Lenovo/Motorola’s Qira wearable agent illustrate a future where AI not only assists but anticipates across devices and contexts. While many of these systems currently rely on richer cloud models, the trajectory clearly points toward edge-first awareness.

This mirrors a broader industry narrative: AI is transitioning from purely digital interactions to physical ones. NVIDIA’s discussions around Physical AI at the show emphasized models and systems that learn to understand space, motion, and cause-and-effect relationships from sensor data, blurring the boundary between perception and action.

For wearables and personal AI agents to meaningfully interpret multimodal data (motion, biometric, environmental) continuously, they require highly efficient, low-latency inference. Neuromorphic processors can complement traditional AI with event-driven architectures that prioritize relevant signals and minimize idle energy use. This is a key advantage for battery-powered wearables and always-listening assistants.

The Architectural Shift Toward Neuromorphic Intelligence

What all these trends, robotics, smart home devices, and autonomous agents have in common is a need for local, real-time intelligence that is both power-efficient and context-aware. The industry is marching toward physical and ambient AI that operates outside the cloud and inside the edge.

Neuromorphic computing architectures are a foundational technology to realize that vision. Instead of relying on synchronous processing and constant connectivity, they embrace event-driven computation, parallel sensory integration, and energy-proportional response — enabling devices to sense and respond like living systems.

We’re excited to see the broader industry catching up to this architectural truth: innovation will increasingly happen at the level of how systems compute, not just what algorithms they run. The way forward is neuromorphic.

If you’re exploring how neuromorphic architectures can unlock new classes of intelligent devices, we’d love to connect. Reach out to start a conversation.