Deep learning has led to unprecedented successes in solving some very difficult problems
in domains such as computer vision, natural language processing, and general pattern …

This paper updates the survey of AI accelerators and processors from past three years. This
paper collects and summarizes the current commercial accelerators that have been publicly …

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 …

In this work, we present a method for optical frequency multiplication utilizing a hybrid deep
learning approach that integrates the Residual Network (ResNet) with the Random Forest …

Deep neural networks (DNNs) have shown to perform impressive humanlike tasks in a
range of applications including image and video processing [43], diagnostic medical …

In recent years, the demand for computational power has skyrocketed due to the rapid
advancement of artificial intelligence (AI). As we move past Moore's Law, the limitations of …

Testing of manufacturing defects has seen a surge recently with growing demands in micron
and sub-micron devices in the semiconductor industry. Among the techniques prevalent in …

Modern computing tasks are constrained to having digital electronic input and output data.
Due to these constraints imposed by the user, any analog computing accelerator must …

Convolutional neural networks (CNNs) represent one of the most effective methods for
image classification. The de-facto approach for performing the required 2D convolutions is to …

This chapter introduces the challenges of computing and data processing in terms of
scalability, speed and power consumption. To address these challenges, Nonvon Neumann …