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

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 …

Abstract Convolutional Neural Networks (CNNs) achieve state-of-the-art performance in
many recognition problems. However, CNN models are computation-intensive and require …

In recent years, Deep Convolutional Neural Network (DCNN) has become the dominant
approach for almost all recognition and detection tasks and outperformed humans on certain …

Stochastic computing (SC) is an alternative computing domain for ubiquitous deterministic
computing whereby a single logic gate can perform the arithmetic operation by exploiting the …

Recent researches have shown that spectral representation provides a significant speed-up
in the massive computation workload of convolution operations in the inference (feed …

The Adiabatic Quantum-Flux-Parametron (AQFP) superconducting technology has been
recently developed, which achieves the highest energy efficiency among superconducting …

Recently, the deep computation model, as a tensor deep learning model, has achieved
super performance for multimedia feature learning. However, the conventional deep …

Deep Convolutional Neural Networks (DCNN), a branch of Deep Neural Networks which
use the deep graph with multiple processing layers, enables the convolutional model to …

The Bayesian method is capable of capturing real-world uncertainties/incompleteness and
properly addressing the overfitting issue faced by deep neural networks. In recent years …