In recent years, convolutional neural networks (CNNs) have shown great performance in
various fields such as image classification, pattern recognition, and multi-media …

Neural networks (NNs) are effective machine learning models that require significant
hardware and energy consumption in their computing process. To implement NNs …

Memristor crossbars are circuits capable of performing analog matrix-vector multiplications,
overcoming the fundamental energy efficiency limitations of digital logic. They have been …

Deep neural networks have evolved remarkably over the past few years and they are
currently the fundamental tools of many intelligent systems. At the same time, the …

Stochastic computing (SC) is an unconventional method of computation that treats data as
probabilities. Typically, each bit of an N-bit stochastic number (SN) Xis randomly chosen to …

With the recent advance of wearable devices and Internet of Things (IoTs), it becomes
attractive to implement the Deep Convolutional Neural Networks (DCNNs) in embedded and …

Convolutional Neural Networks (CNNs) are currently adopted to solve an ever greater
number of problems, ranging from speech recognition to image classification and …

The breakthrough of deep learning has started a technological revolution in various areas
such as object identification, image/video recognition and semantic segmentation. Neural …

Stochastic computing (SC) allows for extremely low cost and low power implementations of
common arithmetic operations. However inherent random fluctuation error and long latency …

This paper develops novel deep learning-based architectures and design methodologies for
an orthogonal frequency division multiplexing (OFDM) receiver under the constraint of one …