As deep neural networks (DNNs) revolutionize machine learning, energy consumption and
throughput are emerging as fundamental limitations of CMOS electronics. This has …

The optical neural network (ONN) is a promising hardware platform for next-generation
neurocomputing due to its high parallelism, low latency, and low energy consumption …

Deep neural networks (DNNs) are reshaping the field of information processing. With the
exponential growth of these DNNs challenging existing computing hardware, optical neural …

Analog photonic computing comprises a promising candidate for accelerating the linear
operations of deep neural networks (DNNs), since it provides ultrahigh bandwidth, low …

Integrated neuromorphic photonic circuits aim to power complex artificial neural networks
(ANNs) in an energy and time efficient way by exploiting the large bandwidth and the low …

There has been growing interest in using photonic processors for performing neural network
inference operations; however, these networks are currently trained using standard digital …

The number of parameters in deep neural networks (DNNs) is scaling at about 5× the rate of
Moore's Law. To sustain this growth, photonic computing is a promising avenue, as it …

Machine learning (ML) architectures such as Deep Neural Networks (DNNs) have achieved
unprecedented accuracy on modern applications such as image classification and speech …

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 …

Optical implementations of neural networks (ONNs) herald the next-generation high-speed
and energy-efficient deep learning computing by harnessing the technical advantages of …