Deep Neural Networks (DNNs) are very popular because of their high performance in
various cognitive tasks in Machine Learning (ML). Recent advancements in DNNs have …

Aiming at a class of switched uncertain nonlinear strict-feedback systems under the action of
average dwell time switching signal, this paper proposes a novel adaptive neural network …

Approximate multipliers are applicable in error-resilient applications with relaxed precision
constraints, including image processing, multimedia, and data recognition. Such multipliers …

In this paper, we present our state-of-the-art approximate techniques that cover the main
pillars of approximate computing research. Our analysis considers both static and …

In this paper, we introduce an energy efficient edge computing solution to collaboratively
utilize Multi-access Edge Computing (MEC) and Fully Autonomous Aerial Systems (FAAS) to …

Addition units are widely used in many computational kernels of several error-tolerant
applications such as machine learning and signal, image, and video processing. Besides …

Current state-of-the-art employs approximate multipliers to address the highly increased
power demands of deep neural network (DNN) accelerators. However, evaluating the …

Transformer-based models achieve tremendous success in many artificial intelligence (AI)
tasks, outperforming conventional convolution neural networks (CNNs) from natural …

This article proposes boosting the multiplication performance for convolutional neural
network (CNN) inference using a precision prediction preprocessor which controls various …

Depthwise separable convolution (DSC) has become one of the essential structures for
lightweight convolutional neural networks. Nevertheless, its hardware architecture has not …