Recently, Capsule Networks (CapsNets) have shown improved performance compared to
the traditional Convolutional Neural Networks (CNNs), by encoding and preserving spatial …

In various applications, such as computer vision and natural language processing, deep
learning is commonly used. End devices like smartphones and sensors on the Internet of …

Recent works have proposed various distributed federated learning (FL) systems for the
edge computing paradigm. These FL algorithms can assist pervasive applications in various …

The potential held by the gargantuan volumes of data being generated across networks
worldwide has been truly unlocked by machine learning techniques and more recently Deep …

High-cost stochastic number generators (SNGs) are the main source of stochastic numbers
(SNs) in stochastic computing. Interacting SNs must usually be uncorrelated for satisfactory …

Deep learning has achieved state-of-the-art performance on several computer vision tasks
and domains. Nevertheless, it still has a high computational cost and demands a significant …

Hardware accelerator-based CNN inference improves the performance and latency but
increases the time-to-market. As a result, CNN deployment on hardware is often outsourced …

Efficient low-power accelerators for Convolutional Neural Networks (CNNs) largely benefit
from quantization and approximation, which are typically applied layer-wise for efficient …

Abstract Among advanced Deep Neural Network models, Capsule Networks (CapsNets)
have shown high learning and generalization capabilities for advanced tasks. Their …

Over the past few years, the idea of edge computing has seen substantial expansion in both
academic and industrial circles. This computing approach has garnered attention due to its …