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

Photonic integrated circuits are facilitating the development of optical neural networks, which
have the potential to be both faster and more energy efficient than their electronic …

In this review paper, we discuss the properties and applications of photonic computing and
analog signal processing. Photonic computational circuits have large operation bandwidth …

The energy demanding nature of deep learning (DL) has fueled the immense attention for
neuromorphic architectures due to their ability to operate in a very high frequencies in a very …

In the modern era of artificial intelligence, increasingly sophisticated artificial neural
networks (ANNs) are implemented, which pose challenges in terms of execution speed and …

While the use of graphic processing units fueled the success of artificial intelligence models,
their future evolution will likely require overcoming the speed and energy efficiency …

The inverse design of photonic integrated circuits (PICs) presents distinctive computational
challenges, including their large memory requirements. Advancements in the two-photon …

In recent years, the field of neural network research has witnessed remarkable
advancements in various domains. One of the emerging approaches is the integration of …

We propose a photonic max-pooling architecture for photonic neural networks which is
compatible with integrated photonic platforms. As a proof of concept, we have …

Photonic computing plays a significant role in high-performance computing applications.
The high speed and capacity of processing larger information by photonic signals assist the …