In this paper, we provide a systematic approach for assessing and comparing the
computational complexity of neural network layers in digital signal processing. We provide …

Recent advancements in machine learning achieved by Deep Neural Networks (DNNs)
have been significant. While demonstrating high accuracy, DNNs are associated with a …

Deep neural networks are the state-of-the-art methods for many real-world tasks, such as
computer vision, natural language processing and speech recognition. For all its popularity …

Although deep learning models are highly effective for various learning tasks, their high
computational costs prohibit the deployment to scenarios where either memory or …

For most deep learning algorithms training is notoriously time consuming. Since most of the
computation in training neural networks is typically spent on floating point multiplications, we …

Epilepsy is a neurological disorder characterised by unusual brain activity widely known as
seizure affecting 4-7% of the world's population. The diagnosis of this disorder is currently …

Currently, deep neural networks are deployed on low-power portable devices by first
training a full-precision model using powerful hardware, and then deriving a corresponding …

Multiplication (eg, convolution) is arguably a cornerstone of modern deep neural networks
(DNNs). However, intensive multiplications cause expensive resource costs that challenge …

Improving the accuracy of a neural network (NN) usually requires using larger hardware that
consumes more energy. However, the error tolerance of NNs and their applications allow …

We investigate two questions in this paper: First, we ask to what extent" MPC friendly"
models are already supported by major Machine Learning frameworks such as TensorFlow …