While vision transformers (ViTs) have continuously achieved new milestones in the field of
computer vision, their sophisticated network architectures with high computation and …

Ising machines based on nonlinear analog systems are a promising method to accelerate
computation of NP-hard optimization problems. Yet, their analog nature is also causing …

Neural networks for industrial applications generally have additional constraints such as
response speed, memory size and power usage. Randomized learners can address some …

This article presents a piecewise linear approximation computation (PLAC) method for all
nonlinear unary functions, which is an enhanced universal and error-flattened piecewise …

Convolutional Neural Networks (CNN) continue to dominate research in the area of
hardware acceleration using Field Programmable Gate Arrays (FPGA), proving its …

The sigmoid function is a widely used nonlinear activation function in neural networks. In this
article, we present a modular approximation methodology for efficient fixed-point hardware …

Despite many decades of research on high-performance Deep Neural Network (DNN)
accelerators, their massive computational demand still requires resource-efficient, optimized …

Accurate segmentation of the pancreas from abdomen scans is crucial for the diagnosis and
treatment of pancreatic diseases. However, the pancreas is a small, soft and elastic …

Sleep apnea is a type of disorder caused by the absence of breathing for a specific period of
time coupled with a significant decrease in the blood oxygen saturation level. The …

In modern computers, complicated signal processing is highly optimized with the use of
compilers and high-speed processing using floating-point units (FPUs); therefore …