BrainChip Enables the Next Generation of Always-On Wearables with the AkidaTag© Reference Platform
New Platform for wearable AI and industrial uses combines the company’s Akida AKD1500 neuromorphic co-processor silicon with Nordic Semiconductor’s nRF5340 wireless SoC for intelligent low-power sensing LAGUNA HILLS, Calif. — March 10, 2026 — BrainChip Holdings Ltd. (ASX: BRN, OTCQX: BRCHF, ADR: BCHPY), the world’s first commercial producer of ultra-low-power, fully digital, event-based neuromorphic AI, today announced at Embedded World in Nuremberg, Germany the launch of AkidaTag©, a reference platform for smart sensing in a battery-powered tag powered by Nordic Semiconductor. Intelligent wearable and remote industrial sensors can deliver always-on sensing on a battery without relying on a connection to a mobile device, PC or the cloud. Offering on-device adaptive learning allows personalization of the model while in use on-device, addressing the challenge of “one-size-fits-all” trained models and reducing the need to transmit data off the device for retraining on a GPU. OEMs can rapidly develop products based on the AkidaTag reference design using design partners. BrainChip AKD1500 operates as a dedicated neuromorphic AI co-processor, delivering optimal energy efficiency while the Nordic nRF5340 wireless SoC handles connectivity, sensor management, and application logic. This provides a foundation for wearables and remote sensing device manufacturers to build devices that can interpret and communicate information effectively. AkidaTag features connectivity through Nordic Semiconductor’s nRF5340 Bluetooth® Low Energy (LE) SoC on-device wireless communications. A BrainChip-developed mobile application utilizes this connection to set up configuration, load, and update models and firmware as well as receive diagnostics, logging, and alerts to the mobile device. AkidaTag offers a blueprint and development kit with full design, hardware, firmware, and mechanicals that enable wearables makers and other manufacturers to build devices that can:- Monitor biological signals for health and wellness applications while preserving privacy through fully on-device processing. Low power usage allows for days of monitoring vital health signals.
- Detect anomalies in vibration and motion for classification in industrial equipment, enabling predictive maintenance.
- Voice wake-up commands for intelligent interfaces.
- Acoustic ambient environment detection and classification.
- Personalization of the device AI model using on-device edge learning and self-learning through neuromorphic principles.
- BrainChip Expands IP Business Model with AKD1500 Production to Accelerate Edge AI Deployment
- Solving Physical AI Challenges
- Use Cases: Low-power AI Applications Across Industries