CLEVER’s research on green 6G AI ensures that as networks scale, they remain energy-efficient and secure. By integrating photonics with AI, we are paving the way for a sustainable digital future, where advanced communication technologies coexist with environmental responsibility.
CLEVER’s research focuses on enhancing the capabilities of edge computing through AI integration. By developing advanced algorithms and middleware, we aim to improve data processing efficiency and decision-making at the edge.
Explore CLEVER’s contributions in the proceedings of the 5th Summer School on Cyber-Physical Systems and Internet-of-Things (SS-CPS&IoT’2024), covering topics from Edge AI acceleration to quality-driven CPS design. These insights are shaping the future of cyber-physical systems and the Internet of…
PANNs are designed to enable real-time cyber defense directly at 6G base stations. By leveraging photonic processing, these networks can detect and mitigate threats with unprecedented speed and efficiency, ensuring robust security for future communication infrastructures.
Photonic CNNs are revolutionizing AI processing by offering superior speed, density, and energy efficiency compared to traditional electronic counterparts. This advancement is crucial for the development of sustainable 6G networks, enabling real-time cyber defense and efficient data processing at the…
CLEVER is pioneering post-quantum cryptography (PQC) solutions, deploying a fully PQC-protected network stack on NVIDIA’s BlueField-2 Data Processing Units (DPUs). This ensures data center line rates with in-line PQC acceleration, preparing infrastructure for the quantum era.
Discover the practical implementation of CATER, our policy-based data placement framework, integrated with Apache Ozone. This demonstrates how external, loosely-coupled components can enhance existing storage systems to meet the demanding requirements of edge computing.The integration with Apache Ozone involved modifying…
CLEVER employs Convolutional Neural Network (CNN) architectures like ResNet, VGG19, and DenseNet for automated, precise debris detection in deep-sea ecosystems. These tools aid in protecting oceans through advanced AI-driven monitoring.
CLEVER’s research investigates GPU virtualization in edge scenarios using the QUIC protocol as a next-gen alternative to TCP. This approach addresses latency issues and enhances GPU utilization, crucial for real-time applications at the edge.
CLEVER’s AI-driven applications enable data-driven decision-making at the edge. By processing data locally, our solutions provide timely insights and actions, enhancing operational efficiency.
