The exponential growth of information stored in data centers and computational power
required for various data-intensive applications, such as deep learning and AI, call for new …

The explosion of artificial intelligence and machine-learning algorithms, connected to the
exponential growth of the exchanged data, is driving a search for novel application-specific …

Advanced machine learning models are currently impossible to run on edge devices such
as smart sensors and unmanned aerial vehicles owing to constraints on power, processing …

Deep neural networks (DNNs) consist of layers of neurons interconnected by synaptic
weights. A high bit-precision in weights is generally required to guarantee high accuracy in …

Deep learning has been a powerful tool in various fields. Faced with the limits of the current
electronics platform, optical neural networks (ONNs) based on Si programmable photonic …

Advances in deep learning research over the past decade have been enabled by an
increasingly unsustainable demand for compute power. This trend has dramatically …

Photonic integrated circuits (PICs) are revolutionizing the realm of information technology,
promising unprecedented speeds and efficiency in data processing and optical …

In recent decades, the demand for computational power has surged, particularly with the
rapid expansion of artificial intelligence (AI). As we navigate the post-Moore's law era, the …

Analog computing has reemerged as a promising avenue for accelerating deep neural
networks (DNNs) to overcome the scalability challenges posed by traditional digital …

Abstract Silicon nitride (Si3N4) photonic integrated circuits (PICs) are of great interest due to
their extremely low propagation loss and higher integration capabilities. The number of …