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This research addresses the intersection of low-power microcontroller technology and binary classification of events in the context of carbon-emission reduction. The study introduces an innovative approach leveraging microcontrollers for real-time event detection in a homogeneous hardware/firmware manner and faced with limited resources. This showcases their efficiency in processing sensor data and reducing power consumption without the need for extensive training sets. Two case studies focusing on landfill CO (Formula presented.) emissions and home energy usage demonstrate the feasibility and effectiveness of this approach. The findings highlight significant power savings achieved by minimizing data transmission during non-event periods (94.8–99.8%), in addition to presenting a sustainable alternative to traditional resource-intensive AI/ML platforms that comparatively draw and produce 20,000 times the amount of power and carbon emissions, respectively. ....
ABI Research's latest whitepaper highlights 37 transformative technology stats you need to know for 2023 and beyond NEW YORK, Feb. 9, 2023 /PRNewswire/ For the past three years, business ....
Quad-Core, Linux-Capable RISC-V Uses Custom Instruction Capability to Achieve AI Acceleration Efinix, an innovator in programmable product platforms and technology, today announced the release of ....
The combination ensures the models are automatically optimized for the Katana ultra-low-power Edge AI SoC platform for designers of IoT devices for vision,. ....