Highlights•Machine learning techniques (MLTs) are progressing the drug discovery
process.•Conventional MLTs require large data, lack transparency and are not …

The advent of softwarized networks has enabled the deployment of chains of virtual network
and service components on computational resources from the cloud up to the edge, creating …

Memristor crossbars are circuits capable of performing analog matrix-vector multiplications,
overcoming the fundamental energy efficiency limitations of digital logic. They have been …

The last decade witnessed a growing interest in Bayesian learning. Yet, the technicality of
the topic and the multitude of ingredients involved therein, besides the complexity of turning …

Artificial neural networks have demonstrated superiority over traditional computing
architectures in tasks such as pattern classification and learning. However, they do not …

Deep Neural Networks (DNNs) have achieved extraordinary performance in various
application domains. To support diverse DNN models, efficient implementations of DNN …

With the rise of artificial intelligence in recent years, Deep Neural Networks (DNNs) have
been widely used in many domains. To achieve high performance and energy efficiency …

Large deep neural network (DNN) models pose the key challenge to energy efficiency due
to the significantly higher energy consumption of off-chip DRAM accesses than arithmetic or …

Memristors, and other emerging memory technologies, can be used to create energy-
efficient implementations of neural networks. However, for certain edge applications (in …

Deep neural network (DNN) accelerators as an example of domain-specific architecture
have demonstrated great success in DNN inference. However, the architecture acceleration …