BrainChip Enables the Next Generation of Always-On Wearables With the AkidaTag© Reference Platform

Key Terms

neuromorphic ai technical

Neuromorphic AI is a style of computing that builds hardware and software to mimic how the human brain processes information, using networks of artificial “neurons” to handle tasks like pattern recognition with low power and fast response. For investors it matters because these systems can enable devices and services that are far more energy-efficient and responsive than conventional processors, potentially opening new markets, lowering operating costs, and creating product differentiation — but they also carry development and adoption risks.

co-processor technical

A co-processor is a specialized chip or circuit that handles particular computing tasks—such as graphics, encryption, or artificial intelligence—so the main processor can focus on general work. Think of it like a kitchen appliance that takes over one repetitive job so the chef can concentrate on the rest of the meal; for investors, co-processors can signal better product performance, lower power use, or differentiation that may drive sales, partnerships, or pricing power.

soc technical

Standard of care (often abbreviated SOC) is the treatment or management approach that is widely accepted and used by medical professionals for a particular disease or condition. For investors, SOC provides the benchmark against which new therapies, devices, or clinical results are judged—like comparing a new car to the current most popular model; a product that meaningfully outperforms the SOC can win market share and drive revenue, while failure to beat or match it limits commercial potential.

bluetooth low energy technical

Bluetooth Low Energy is a wireless communication standard that lets small devices exchange short bursts of data over short distances while using very little battery power. For investors it matters because it enables products like fitness trackers, smart-home sensors and wireless accessories to run longer on small batteries and connect seamlessly, affecting product appeal, component demand, licensing and overall market growth—like a whisper that makes many gadgets practical.

on-device edge learning technical

On-device edge learning is when a device such as a smartphone, sensor, or factory machine updates its own AI behaviors locally instead of sending data to a distant server. Think of it like a person practicing a skill at home rather than mailing their homework to a teacher: it speeds up responses, uses less network bandwidth, and keeps personal data private. Investors care because it can lower operating costs, enable new products, shift demand toward specialized chips and software, and reduce regulatory risk related to data handling.

predictive maintenance technical

Predictive maintenance involves using data and technology to monitor equipment or machinery in real time, identifying potential problems before they cause failures or breakdowns. By predicting when maintenance is needed, it helps prevent costly repairs and downtime. For investors, it highlights how companies can reduce expenses, improve efficiency, and maintain reliable operations, which can positively impact financial performance.

accelerometer technical

An accelerometer is a small sensor that measures changes in motion, direction and tilt—think of it as the device that tells a smartphone when you rotate it or a wearable when you start running. For investors, accelerometers matter because they are key components in many products (phones, cars, medical devices, industrial machines) and influence a company’s product features, manufacturing costs, supply-chain risk and market opportunities, affecting sales and competitive position.

keyword spotting technical

Keyword spotting is the process of scanning news, filings or audio and flagging specific words or short phrases that may signal important developments for a company, such as earnings, bankruptcy, merger, or drug approval. For investors it acts like a smoke detector: it quickly highlights potentially market-moving language so traders and analysts can review items faster and decide whether to act. It speeds up monitoring across large volumes of documents and broadcasts.

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.--(BUSINESS WIRE)-- 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.

“At BrainChip, we are committed to pushing the boundaries of what is possible at the edge,” said Sean Hehir, CEO of BrainChip. “By leveraging the robust ecosystem of Nordic Semiconductor, we are providing product designers with a blueprint for the future of wearables that’s always-on, privacy-first, and self-learning. This platform proves that high-performance AI and long battery life are no longer mutually exclusive.”

“Seeing BrainChip use our technology to enable always-on neuromorphic AI demonstrates the potential of our wireless SoCs to drive the next wave of innovation in wearable and connected health markets,” said Petter Myhre, Product Marketing Director at Nordic Semiconductor. “The nRF5340 is the world’s first wireless SoC with two Arm® Cortex®-M33 processors, making it the perfect choice for complex IoT applications that require both high performance and extreme energy efficiency.”

BrainChip will highlight the AkidaTag’s capabilities at Embedded World (Hall 5, booth 5-213) through a self-contained battery-powered “puck.” This will feature an integrated accelerometer for motion detection and a microphone for voice and vibration recognition. It can perform various functions, including keyword spotting, anomaly detection, and voice-activated wake-up. Control is through a mobile app that dynamically displays results and enables users to download activities for analysis.

Availability: The reference platform is licensed to accelerate development for OEMs and system integrators; including hardware schematics, firmware, and a companion mobile application. It will be available for evaluation in May 2026, with volume in Q3 2026.

Additional resources:

• 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

About BrainChip Holdings Ltd:

BrainChip is the worldwide leader in Edge AI on-chip processing and learning. The company’s first-to-market, fully digital, event-based AI processor, Akida™ uses neuromorphic principles to mimic the human brain, analyzing only essential sensor inputs at the point of acquisition and processing data with unmatched efficiency, precision, and energy economy. BrainChip’s Temporal Event-based Neural Networks (TENNs) build on State-Space Models (SSMs) with time-sensitive, event-driven frameworks that are ideal for real-time streaming applications. These innovations make low-power Edge AI deployable across industries such as aerospace, autonomous vehicles, robotics, industrial IoT, consumer devices, and wearables. BrainChip is advancing the future of intelligent computing, bringing AI closer to the sensor and closer to real-time. Explore more at www.brainchip.com. Follow BrainChip on X or LinkedIn.

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BrainChip Media Contact

Madeline Coe

prforbrainchip@bospar.com

224-433-9056

BrainChip Investor Contact

ir@brainchip.com

Source: BrainChip Holdings Ltd