Stochastic computing (SC) has a substantial amount of study on application-specific
integrated circuit (ASIC) design for artificial intelligence (AI) edge computing, especially the …

Brain tumors are nonlinear and present with variations in their size, form, and textural
variation; this might make it difficult to diagnose them and perform surgical excision using …

Malaria disease should be considered and handled as a potential restorative catastrophe.
One of the most challenging tasks in the field of microscopy image processing is due to …

There is a growing interest in automated neural architecture search (NAS) methods. They
are employed to routinely deliver high-quality neural network architectures for various …

In the early design phase of a Deep Neural Network (DNN) acceleration system, fast energy
and latency estimation are important to evaluate the optimality of different design candidates …

In the past few years, Capsule Networks (CapsNets) have taken the spotlight compared to
traditional convolutional neural networks (CNNs) for image classification. Unlike CNNs …

Abstract Intelligence Processing Units (IPU) have proven useful for many AI applications. In
this paper, we evaluate them within the emerging field of AI for simulation, where traditional …

Deep neural networks are widely deployed on embedded devices to solve a wide range of
problems from edge-sensing to autonomous driving. The accuracy of these networks is …

Recently, Deep Neural Networks (DNNs) have achieved remarkable performances in many
applications, while several studies have enhanced their vulnerabilities to malicious attacks …

Artificial intelligence (AI) algorithms are extremely computational-intensive on voluminous
data. AI accelerators are desired to satisfy their hardware demands. This chapter introduces …