Research has shown that convolutional neural networks contain significant redundancy, and
high classification accuracy can be obtained even when weights and activations are …

We present a memory and computation efficient ternary weight networks (TWNs)-with
weights constrained to+ 1, 0 and-1. The Euclidian distance between full (float or double) …

Binarized neural networks (BNNs) are gaining interest in the deep learning community due
to their significantly lower computational and memory cost. They are particularly well suited …

The Internet of bio-nano things (IoBNT) is an emerging paradigm employing nanoscale (~ 1–
100 nm) biological transceivers to collect in vivo signaling information from the human body …

The development of novel hardware computing systems and methods has been a topic of
increased interest for researchers worldwide. New materials, devices, and architectures are …

This article provides a comprehensive review of recent developments in the field of
computational hardware for mobile low power machine learning hardware accelerators. The …

Brain is a very efficient computing system. It performs very complex tasks while occupying
about 2 liters of volume and consuming very little energy. The computation tasks are …

Modern machine learning models are beginning to rival human performance on some
realistic object recognition tasks, but we still lack a full understanding of how the human …

Deep neural networks have demonstrated impressive results in various cognitive tasks such
as object detection and image classification. This paper describes a neuromorphic …

In recent era, emotion recognition plays a vital role in human–machine interaction. Service
robots identify the significant aspects of human behavior by analyzing the emotions of …