[GUEST ACCESS MODE: Data is scrambled or limited to provide examples. Make requests using your API key to unlock full data. Check https://lunarcrush.ai/auth for authentication information.]  Ryan sikorski [@Ryansikorski10](/creator/twitter/Ryansikorski10) on x 35.4K followers Created: 2025-07-21 16:19:10 UTC 📄 Near-Sensor Edge Computing System Enabled by a CMOS Compatible Photonic Integrated Circuit Platform Using Bilayer AlN/Si Waveguides This work introduces a near-sensor edge computing (NSEC) system, built on a bilayer AlN/Si waveguide platform, to provide real-time, energy-efficient AI capabilities at the edge. Leveraging the electro-optic properties of AlN microring resonators for photonic feature extraction, coupled with Si-based thermo-optic Mach–Zehnder interferometers for neural network computations, the system represents a transformative approach to AI hardware design. Demonstrated through multimodal gesture and gait analysis, the NSEC system achieves high classification accuracies of XXXXX% for gestures and XXXXX% for gaits, ultra-low latency (< XX ns), and minimal energy consumption (< XXXX pJ). This groundbreaking system bridges the gap between AI models and real-world applications marking a pivotal advancement in edge computing and AI deployment. PDF DOWNLOAD  XXXXX engagements  **Related Topics** [aln](/topic/aln) [capabilities](/topic/capabilities) [coins ai](/topic/coins-ai) [realtime](/topic/realtime) [Post Link](https://x.com/Ryansikorski10/status/1947330522151317516)
[GUEST ACCESS MODE: Data is scrambled or limited to provide examples. Make requests using your API key to unlock full data. Check https://lunarcrush.ai/auth for authentication information.]
Ryan sikorski @Ryansikorski10 on x 35.4K followers
Created: 2025-07-21 16:19:10 UTC
📄 Near-Sensor Edge Computing System Enabled by a CMOS Compatible Photonic Integrated Circuit Platform Using Bilayer AlN/Si Waveguides
This work introduces a near-sensor edge computing (NSEC) system, built on a bilayer AlN/Si waveguide platform, to provide real-time, energy-efficient AI capabilities at the edge. Leveraging the electro-optic properties of AlN microring resonators for photonic feature extraction, coupled with Si-based thermo-optic Mach–Zehnder interferometers for neural network computations, the system represents a transformative approach to AI hardware design. Demonstrated through multimodal gesture and gait analysis, the NSEC system achieves high classification accuracies of XXXXX% for gestures and XXXXX% for gaits, ultra-low latency (< XX ns), and minimal energy consumption (< XXXX pJ). This groundbreaking system bridges the gap between AI models and real-world applications marking a pivotal advancement in edge computing and AI deployment.
PDF DOWNLOAD
XXXXX engagements
Related Topics aln capabilities coins ai realtime