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Jeff Dix, Jeremy Holleman and Benjamin J. Blalock
A programmable, energy-efficient analog hardware implementation of a multilayer perceptron (MLP) is presented featuring a highly programmable system that offers the user the capability to create an MLP neural network hardware design within the available ...
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Ahmed El-Mesady, Aleksandr Y. Romanov, Aleksandr A. Amerikanov and Alexander D. Ivannikov
Recent developments in commutative algebra, linear algebra, and graph theory allow us to approach various issues in several fields. Circulant graphs now have a wider range of practical uses, including as the foundation for optical networks, discrete cell...
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Duc-Minh Ngo, Dominic Lightbody, Andriy Temko, Cuong Pham-Quoc, Ngoc-Thinh Tran, Colin C. Murphy and Emanuel Popovici
This study proposes a heterogeneous hardware-based framework for network intrusion detection using lightweight artificial neural network models. With the increase in the volume of exchanged data, IoT networks? security has become a crucial issue. Anomaly...
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Andreas G. Savva, Theocharis Theocharides and Chrysostomos Nicopoulos
Nowadays, due to their excellent prediction capabilities, the use of artificial neural networks (ANNs) in software has significantly increased. One of the most important aspects of ANNs is robustness. Most existing studies on robustness focus on adversar...
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Guillaume Devic, Gilles Sassatelli and Abdoulaye Gamatié
The execution of machine learning (ML) algorithms on resource-constrained embedded systems is very challenging in edge computing. To address this issue, ML accelerators are among the most efficient solutions. They are the result of aggressive architectur...
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Zeeshan Ali Khan, Ubaid Abbasi and Sung Won Kim
Network-on-chip (NoC) is replacing the existing on-chip communication mechanism in the latest, very-large-scale integration (VLSI) systems because of their fault tolerant design. However, in addition to the design challenges, NoC systems require a mechan...
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Seojin Jang, Wei Liu and Yongbeom Cho
Owing to their high accuracy, deep convolutional neural networks (CNNs) are extensively used. However, they are characterized by high complexity. Real-time performance and acceleration are required in current CNN systems. A graphics processing unit (GPU)...
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Somdip Dey, Amit Kumar Singh and Klaus McDonald-Maier
Side-channel attacks remain a challenge to information flow control and security in mobile edge devices till this date. One such important security flaw could be exploited through temperature side-channel attacks, where heat dissipation and propagation f...
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Matteo Grimaldi, Valerio Tenace and Andrea Calimera
Convolutional Neural Networks (CNNs) are brain-inspired computational models designed to recognize patterns. Recent advances demonstrate that CNNs are able to achieve, and often exceed, human capabilities in many application domains. Made of several mill...
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