Deep Learning algorithms have achieved state-of-the-art performance for Image
Classification. For this reason, they have been used even in security-critical applications …

With the increasing stringent QoS constraints (eg, latency, bandwidth, jitter) imposed by
novel applications (eg, e-Health, autonomous vehicles, smart cities, etc.), as well as the …

Tiny machine learning technologies are bringing intelligence ever closer to the sensor, thus
enabling the key benefits of edge computing (eg, reduced latency, improved data security …

We propose a new algorithm for training deep neural networks (DNNs) with binary weights.
In particular, we first cast the problem of training binary neural networks (BiNNs) as a bilevel …

To alleviate the practical constraints for deploying deep neural networks (DNNs) on edge
devices, quantization is widely regarded as one promising technique. It reduces the …

Deep learning with edge computing arises as a popular paradigm for powering edge
devices with intelligence. As the size of deep neural networks (DNN) continually increases …

This paper compares the latency, accuracy, training time and hardware costs of neural
networks compressed with our new multi-objective evolutionary algorithm called NEMOKD …

Reducing the size of neural network models is a critical step in moving AI from a cloud-
centric to an edge-centric (ie on-device) compute paradigm. This shift from cloud to edge is …

We solve the analysis sparse coding problem considering a combination of convex and non-
convex sparsity promoting penalties. The multi-penalty formulation results in an iterative …

Although machine learning (ML)-based models are increasingly being used by cloud-based
data-driven services, two key problems exist when used at the edge. First, the size and …