Atmosic And AONDevices Announce Strategic Partnership To Leverage Both The Benefits of AI And Energy Harvesting To Lower Power Consumption In AIoT Smart Home Devices
With the strategic alliance that it has formed with AONDevices, Atmosic Technologies will leverage low-power AI processors and ultra-low-power wireless technologies to create wearable and AIoT (Artificial Intelligence of Things) applications that are cutting edge. The ATM Series from Atmosic, renowned for its exceptionally low-power wireless System on Chip (SoC), will combine with the AON series of AI processors from AONDevices to deliver precise voice command recognition, sound awareness, and contextual awareness while consuming the least amount of energy possible. By utilizing energy-efficient solutions to lessen reliance on batteries and further minimizing power use with AONDevices' AI technology, this partnership is in line with Atmosic's commitment to IoT sustainability. Headsets, wearables, game controllers, toys, cars, smart home appliances, smart buildings, and industrial IoT applications are among the many applications that the cooperation aims to address.
The ATM Series wireless SoC from Atmosic combines 802.15.4 protocols, energy harvesting, power management, and RF radios that support Bluetooth Low Energy. The AI processors from AONDevices feature multi-sensor fusion, sound awareness, and voice command recognition with high accuracy under a variety of scenarios. With adaptive speech activity detection, the technology supports speaker independence in always-on and multi-word detection scenarios while assisting with voice control and context detection.
By lowering RF radio power usage and using harvested energy to power sensors, tags, remotes, and other devices, Atmosic has been a pioneer in the IoT sustainability space. By using less batteries and lessening their influence on the environment, this strategy enables manufacturers to produce goods that are friendly to the environment. Through the relationship with AONDevices, power consumption will be further reduced by addressing energy waste during sensor false detections.
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