Matrix computation, as a fundamental building block of information processing in science
and technology, contributes most of the computational overheads in modern signal …

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 …

The relentless growth of Artificial Intelligence (AI) workloads has fueled the drive towards
non-Von Neuman architectures and custom computing hardware. Neuromorphic photonic …

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 …

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 …