Convolutional Neural Networks (CNN) have shown impressive performance in computer
vision, natural language processing, and many other applications, but they exhibit high …

The traditional convolution neural networks (CNN) have several drawbacks like the" Picasso
effect" and the loss of information by the pooling layer. The Capsule network (CapsNet) was …

Perceptron is an essential element in neural network (NN)-based machine learning,
however, the effectiveness of various implementations by circuits is rarely demonstrated …

Convolutional Neural Networks (CNN) has found successful adoption in many applications.
The deployment of CNN on resource-constrained edge devices have proved challenging …

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 …

Abstract Systems and methods are provided for an improved machine learning (ML) model
system. The improved ML system can be configured to (1) initially classify the types of …

With the growing demand for real-time intelligence driven by device-to-device (D2D)
communication, deploying Deep Learning (DL) applications at the network edge becomes …

Convolutional Neural Networks (CNNs) have demonstrated impressive performance in
recent times and have shown a wide range of applicability. The deployment of CNNs on …

Modern applications on general purpose processors require both rapid and power-efficient
computing and memory components. As applications continue to improve, the demand for …

Abstract Internet of Things (IoT) devices has connected millions of houses around the globe
via the internet. Running Artificial Intelligence (AI) on IoT devices is popular nowadays …