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The NeuroSense AI Chip, an ultra-low power neuromorphic frontend, is engineered for wearables to address the challenges of power efficiency and data accuracy in health monitoring applications. This tiny AI chip is designed to process data directly at the sensor level, which includes tasks like heart rate measurement and human activity recognition. By performing computations locally, NeuroSense minimizes the need for cloud connections, thereby ensuring privacy and prolonging battery life. The chip excels in accuracy, significantly outperforming conventional algorithm-based solutions by offering three times better heart rate accuracy. This is achieved through its ability to reduce power consumption to below 100µW, allowing users to experience extended device operation without frequent recharging. The NeuroSense supports a simple configuration setup, making it suitable for integration into a variety of wearable devices such as fitness trackers, smartwatches, and health monitors. Its capabilities extend to advanced features like activity matrices, enabling devices to learn new human activities and classify tasks according to intensity levels. Additional functions include monitoring parameters like oxygen saturation and arrhythmia, enhancing the utility of wearable devices in providing comprehensive health insights. The chip's integration leads to reduced manufacturing costs, a smaller IC footprint, and a rapid time-to-market for new products.
The VibroSense AI Chip is tailored for vibration analysis applications, providing ultra-low power neuromorphic processing capabilities to industrial IoT systems. This chip excels in converting complex vibration data into manageable patterns, facilitating efficient local processing and dramatically reducing data transfer volumes. It is especially valuable in applications such as tire and machine health monitoring, where real-time data analysis can significantly enhance safety and maintenance efficiency. VibroSense enables direct on-sensor preprocessing, transforming high-frequency vibration signals into concise data sets for further evaluation. This not only diminishes the data load on network systems but also supports long-distance, low-bandwidth communications crucial for remote monitoring tasks. Implementing VibroSense in road condition monitoring for automotive applications ensures quick and precise feedback to advanced driver assistance systems, contributing to improved vehicular safety. The chip's ability to predict maintenance needs through vibration-based monitoring makes it a powerful tool in industrial applications. By analyzing vibrations, it can detect potential machinery issues, thus optimizing operational uptime and reducing maintenance costs. The VibroSense chip's integration encourages the development of energy-efficient, reliable predictive maintenance solutions in complex industrial settings.
The NeuroVoice AI Chip offers a revolutionary solution for voice processing, harnessing neuromorphic frontend technology to provide ultra-low power consumption and superior noise resilience. It is designed for hearables and smart voice-controlled devices, ensuring efficient operation even in high-noise environments. This chip processes audio data on-device, eliminating the need for continuous cloud connectivity while enhancing user privacy. By integrating NASP technology, the NeuroVoice chip excels in voice activity detection, smart voice control, and voice extraction, making it ideal for applications in earbuds, voice access systems, and smart home devices. Its ability to only transmit or recognize human voice while muting background sounds significantly improves command clarity and user interactions, especially in environments prone to irregular noises. The chip is designed to adapt to various audio inputs, providing capabilities for clear communication, enhancing speech intelligibility, and offering features like voice passthrough in hearing aids. With power consumption kept below 150µW, it allows for prolonged device usage and efficient battery management, making it an ideal component for modern voice-activated devices and hearing assistance technologies.
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