I Krauhausen, CT Coen, S Spolaor… - Advanced Functional …, 2024 - Wiley Online Library
Neuromorphic computing offers the opportunity to curtail the huge energy demands of modern artificial intelligence (AI) applications by implementing computations into new, brain …
Integrated photonic neural networks provide a promising platform for energy-efficient, high- throughput machine learning with extensive scientific and commercial applications. Photonic …
As deep neural networks (DNNs) revolutionize machine learning, energy consumption and throughput are emerging as fundamental limitations of CMOS electronics. This has …
In these Lecture Notes, we provide a comprehensive introduction to the most recent advances in the application of machine learning methods in quantum sciences. We cover …
We present an adaptive optical neural network based on a large-scale event-driven architecture. In addition to changing the synaptic weights (synaptic plasticity), the optical …
M Moralis-Pegios… - Journal of Lightwave …, 2022 - ieeexplore.ieee.org
The relentless growth of Artificial Intelligence (AI) workloads has fueled the drive towards non-Von Neuman architectures and custom computing hardware. Neuromorphic photonic …
J Huang, B Meng, L Chen, X Wang, X Qu, M Fan… - Physical Review B, 2022 - APS
Actively tunable nanodevices play an important role in modern photonics systems. Metasurfaces based on phase-change materials (PCMs) provide a new way for the …
Reinforcement learning (RL) stands as one of the three fundamental paradigms within machine learning and has made a substantial leap to build general-purpose learning …
M Trik, S Pour Mozaffari… - Computational …, 2021 - Wiley Online Library
Effective and efficient routing is one of the most important parts of routing in NoC‐based neuromorphic systems. In fact, this communication structure connects different units through …