Imagine harnessing the speed of light to power neural networks! At the CLEVER Project, we’re breaking barriers with Optical Convolutional Neural Networks (OCNNs)—a groundbreaking approach to solving the limitations of traditional electronic CNNs. 🔍 Why OCNNs? 💡 Our Vision: TUE…
🎯 What’s the focus? Our work dives deep into AI-enabled optimization of critical computing infrastructures, leveraging:✅ Graph-based system modeling✅ Multi-agent Deep Q-Learning✅ Smart orchestration across the IoT–Edge–Cloud continuum We demonstrate how intelligent agents can dynamically allocate resources, manage dependencies between microservices,…
✅ CLEVER Project @ EDGE AI Conference (Athens, 2023) 🚀 Thrilled to share highlights from our recent presentation at the EDGE AI Conference in Athens, where we showcased how AI can optimize resource orchestration in critical computing infrastructures—a key goal…
Abstract: The proliferation of the Internet of Things (IoT), artificial intelligence (AI), the adoption of 5G, and progress towards 6G technology have led to the accumulation of massive amounts of real-world data; however, a significant portion of the data generated…
Abstract: Cities serve as vital hubs of economic activity and knowledge generation and dissemination. As such, cities bear a significant responsibility to uphold environmental protection measures while promoting the welfare and living comfort of their residents .There are diverse views…
Abstract :Data Processing Units (DPUs) with embedded GPU have the potential to rev- olutionize optical networks functionalities at the edge. Use cases are presented for optical data monitoring with local AI processing and embedded security.
Abstract :The seamless orchestration of resources across the edge-cloud continuum has become a cornerstone in modern critical computing infrastructures. Artificial Intelligence (AI) plays a pivotal role in enabling the fast and efficient management of diverse and demanding workloads. However, maintaining…
Abstract :In distributed environments, data for Machine Learning (ML) applications may be generated from numerous sources and devices, and traverse a cloud-edge continuum via a variety of protocols, using multiple security schemes and equipment types. While ML models typically benefit…
Abstract: We demonstrate the first 92.3-Gbits/s line-rate, end-to-end post-quantum cryptography optical fiber link based on HW accelerators and processing offloading. © 2024The Author(s)
