Deep Neural Networks (DNNs) are nowadays a common practice in most of the Artificial
Intelligence (AI) applications. Their ability to go beyond human precision has made these …

In recent years, it has been witnessed that the systolic array is a successful architecture for
DNN hardware accelerators. However, the design of systolic arrays also encountered many …

Currently, Machine Learning (ML) is becoming ubiquitous in everyday life. Deep Learning
(DL) is already present in many applications ranging from computer vision for medicine to …

Spiking Neural Networks (SNNs), the third generation NNs, have come under the spotlight
for machine learning based applications due to their biological plausibility and reduced …

In the Machine Learning era, Deep Neural Networks (DNNs) have taken the spotlight, due to
their unmatchable performance in several applications, such as image processing, computer …

The security and privacy concerns along with the amount of data that is required to be
processed on regular basis has pushed processing to the edge of the computing systems …

Pharmaceuticals are chemical compounds employed as medicinal drugs. They have severe
physic-chemical properties which make them destructive for non-target species …

Deep neural networks (DNNs) have proliferated in most of the application domains that
involve data processing, predictive analysis and knowledge inference. Alongside the need …

Designing resource-efficient deep neural networks (DNNs) is a challenging task due to the
enormous diversity of applications as well as their time-consuming design, training …

Many convolutional neural network (CNN) accelerators face performance-and energy-
efficiency challenges which are crucial for embedded implementations, due to high DRAM …