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The NeuroVoice AI Chip is a compact, ultra-low-power neuromorphic processor designed to revolutionize voice processing for hearables and smart devices. It directly addresses common hearable concerns such as high power consumption, inefficient noise handling, and privacy issues by performing voice activity detection and processing on-device without cloud dependency. This chip is engineered to recognize and transmit only the human voice, effectively filtering out background noise in real-time. This feature ensures privacy and reduces connectivity costs as all processing is done locally, providing users with a seamless and efficient experience. The NeuroVoice chip is particularly suited for applications like smart home devices, wearables, and mobile gadgets where instant and reliable voice processing is essential. Its ability to operate with ultra-low power consumption makes it an ideal choice for always-on applications, enhancing device battery life and ensuring rapid user interactions.
The NeuroSense AI Chip is tailored to optimize wearables by incorporating advanced neuromorphic processing technology for low-power and high-accuracy data management. Targeting mass-market wearable challenges such as short battery life and low tracking precision, this chip elevates the performance of fitness trackers, smartwatches, and health monitoring devices. It offers remarkable improvements in heart rate measurement accuracy and human activity recognition by processing data directly at the sensor level. This functionality removes the dependency on continuous cloud connectivity, thus enhancing battery life and operational reliability. NeuroSense integrates easily into wearable devices, providing robust solutions for monitoring health indicators like heart rate and verifying human activity patterns. Its compact size and efficient processing make it a significant advancement for wearables, enabling extended device operation while maintaining precise biometric data accuracy.
The VibroSense AI Chip for Tire Monitoring is an innovative, ultra-low-power neuromorphic front-end chip that enhances vehicle safety by offering real-time tire-road friction monitoring. Integrated into smart tire systems, this chip detects changes in road surface conditions and communicates these to the vehicle's Advanced Driving Assistance Systems (ADAS). Road surface awareness is a critical missing element in most vehicle safety systems today. By providing accurate tire-road friction data, the VibroSense chip allows for immediate transmission of alerts to ADAS, significantly improving driving safety by reducing vehicle response time during friction changes. This advanced capability avoids the inaccuracies and delays of traditional indirect friction estimation methods. The chip's design focuses on energy efficiency, supporting extended operation with standard batteries or energy harvesting techniques, fitting easily within existing Tire Pressure Monitoring Systems (TPMS). The solution has been rigorously tested across diverse tires, vehicles, and road types, proving its ability to deliver high-precision monitoring within tire systems at a minimal cost.
The VibroSense Tiny AI Chip is a cutting-edge neuromorphic front-end chip specifically designed for preprocessing vibration data, crucial for Industrial IoT (IIoT) applications. It tackles common challenges such as high data volume and transmission costs by performing real-time processing at the sensor level. This innovative approach reduces the required bandwidth and data storage, making predictive maintenance and machine health monitoring more cost-effective and efficient. Vibration analysis is fundamental for predictive maintenance, enabling early detection of mechanical issues like imbalance, bearing failures, and gear wear. Traditional processing methods involve collecting vast amounts of vibration data, which is costly to transmit and store. The VibroSense chip alleviates these burdens by reducing data volumes by 1000 to 4000 times and facilitating long-distance, narrow-bandwidth communication. This chip supports real-time applications by integrating neural networks to preprocess complex vibration signals locally, even in the presence of noise. This ability ensures precise data handling and reduces reliance on cloud processing, thereby enhancing response times, reliability, and overall system efficiency in industrial environments.
Discover how POLYN Technology redefines edge AI by leveraging its NASP tech to push the frontiers of compact, energy-efficient data processing in its latest accolade. Read more
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