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The NeuroVoice chip by Polyn Technology is engineered to improve voice processing capabilities for a variety of consumer electronic devices, particularly focusing on addressing challenges associated with traditional digital voice solutions. Built on the NASP platform, this AI chip is tailored to operate efficiently in noisy environments without relying on cloud-based processing, thus ensuring privacy and reducing latency. A key feature of NeuroVoice is its ultra-low power consumption, which allows continuous device operation even in power-sensitive applications like wearables and smart home devices. It includes abilities such as always-on voice activity detection, smart voice control, speaker recognition, and real-time voice extraction. This amalgamation of capabilities makes the NeuroVoice a versatile component in enhancing voice-controlled systems' efficacy. NeuroVoice stands out by seamlessly integrating into devices, offering users the advantage of precise voice recognition and activity detection with minimal energy demands. It further differentiates itself by delivering clear communication even amidst irregular background noises, setting a new benchmark for on-device audio processing with its advanced neural network-driven design.
The NeuroSense is a compact AI chip designed specifically for wearable devices, featuring neuromorphic analog signal processing technology. Its main focus lies in resolving common challenges faced by wearable tech, such as high power consumption, and limited battery life. By enabling highly accurate heart rate monitoring and activity recognition, this chip facilitates better fitness tracking without excessively draining battery resources. The NeuroSense's capability of operating independently from cloud connections addresses significant privacy concerns and data latency issues. It excels in delivering enhanced accuracy in heart rate measurements by utilizing a simple photoplethysmogram (PPG) configuration, which involves minimalistic hardware components like two LEDs and one photodiode. Through this setup, it achieves precision in bio-signal extraction far beyond conventional algorithmic methods, particularly when the wearer is in motion. Furthermore, the NeuroSense empowers wearables with advanced features like learning and recognizing user-specific activity patterns. With ultra-low power consumption and a compact size, the NeuroSense enables manufacturers to preserve space within constrained wearable designs while simultaneously enhancing battery life—solving a key concern in the realm of constantly operating smart devices.
The VibroSense chip is a refined AI solution intended for Industrial Internet of Things (IIoT) applications, specializing in vibration data processing to enhance machine health monitoring. By leveraging its neuromorphic analog signal processing capabilities, it pre-processes sensor data at the node, thereby dramatically reducing the data volume that needs transmission and processing. This cutback is pivotal for making predictive maintenance more cost-effective and practical, as it slashes operational costs associated with extensive data handling. VibroSense is particularly advantageous for vibration-based condition monitoring systems. It processes raw data on-sensor, decreasing the data that must be transmitted by a significant margin—a reduction of up to 1000 times. This efficiency not only curbs expenses but also aids in extending the life of communications infrastructure by supporting narrow-bandwidth communication over vast distances. This AI chip enables users to detect a myriad of machine issues, from bearing failures to gear wear, all while being energy-efficient enough to support energy-harvesting designs. This gives it a distinct advantage in the burgeoning field of predictive maintenance (PdM), where accuracy and quick response times are paramount.
The VibroSense AI chip designed for tire monitoring offers an innovative approach to enhancing vehicle safety through real-time analysis of tire-road interactions. This ultra-low power neuromorphic chip seamlessly integrates with traditional Tire Pressure Monitoring Systems (TPMS), enabling the detection of tire-road frictional variations and delivering critical alerts to Advanced Driving Assistance Systems (ADAS). Through its ability to detect changes in the peak friction coefficient (PFC) in real time, VibroSense optimizes vehicle safety significantly. This capability is crucial for achieving instant responsiveness in critical conditions, a feat accomplished by minimizing the wireless communication required with the vehicle. As a result, it preserves battery life and supports designs based on standard or energy-harvesting batteries, making smart tire implementations more feasible and effective. The chip enhances vehicular safety by detecting tire-road friction locally, offering a power-efficient, straightforward integration method into existing TPMS setups. Its advanced detection capabilities augment traditional systems, providing more accurate and timely road condition information to enhance the safety and stability of autonomous and driver-assisted vehicles.
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