Mobile devices such as smartphones and autonomous vehicles increasingly rely on deep
neural networks (DNNs) to execute complex inference tasks such as image classification …

Deep neural networks (DNNs) typically have a single exit point that makes predictions by
running the entire stack of neural layers. Since not all inputs require the same amount of …

The high number of vehicle–pedestrian crashes in the United State has gained increased
attention among transportation safety analysts in recent years. Being directly exposed to the …

Recent breakthroughs in artificial intelligence promote the development of deep neural
networks (DNNs)-based intelligent applications in the Internet of Things (IoT). However …

In this paper, we propose Code-Bridged Classifier (CBC), a framework for making a
Convolutional Neural Network (CNNs) robust against adversarial attacks without increasing …

Modern and recent architectures of vision-based Convolutional Neural Networks (CNN)
have improved detection and prediction accuracy significantly. However, these algorithms …

This paper proposes an ensemble learning model that is resistant to adversarial attacks. To
build resilience, we introduced a training process where each member learns a radically …

In this paper, we present NESTA, a specialized Neural engine that significantly accelerates
the computation of convolution layers in a deep convolutional neural network, while …

In this paper, we first propose the design of Temporal-Carry-deferring MAC (TCD-MAC) and
illustrate how our proposed solution can gain significant energy and performance benefit …

Deploying Machine learning (ML) on milliwatt-scale edge devices (tinyML) is gaining
popularity due to recent breakthroughs in ML and Internet of Things (IoT). Most tinyML …