There has been a resurgence of interest in optical computing since the early 2010s, both in
academia and in industry, with much of the excitement centred around special-purpose …

The single-layer perceptron, introduced by Rosenblatt in 1958, is one of the earliest and
simplest neural network models. However, it is incapable of classifying linearly inseparable …

Photonic computing enables faster and more energy-efficient processing of vision data,,,–.
However, experimental superiority of deployable systems remains a challenge because of …

Deep learning has become a widespread tool in both science and industry. However,
continued progress is hampered by the rapid growth in energy costs of ever-larger deep …

The explosive growth of deep learning applications has triggered a new era in computing
hardware, targeting the efficient deployment of multiply-and-accumulate operations. In this …

A practical limit to energy efficiency in computation is ultimately from noise, with quantum
noise [1] as the fundamental floor. Analog physical neural networks [2], which hold promise …

Physical neural networks are promising candidates for next generation artificial intelligence
hardware. In such architectures, neurons and connections are physically realized and do not …

We show that multiplexing (Here, the term “multiplexing” means a special network topology
where a one-layer network is connected to another one-layer networks through coupling …

In recent years, the hardware implementation of neural networks, leveraging physical
coupling and analog neurons has substantially increased in relevance. Such nonlinear and …

We show how multiplexing influences propagating fronts in multilayer networks of coupled
bistable oscillators. Using numerical simulation, we investigate both deterministic and noise …