Convolutional neural networks (CNNs) are used in our daily life, including self-driving cars,
virtual assistants, social network services, healthcare services, and face recognition, among …

Ternary Neural Networks (TNNs) and mixed-precision Ternary Binary Networks (TBNs) have
demonstrated higher accuracy compared to Binary Neural Networks (BNNs) while providing …

General Matrix Multiplication (GEMM) is a computationally expensive operation that is used
in many applications such as machine learning. Hardware accelerators are increasingly …

Machine learning has shown tremendous success in a large variety of applications. The
evolution of machine-learning applications from cloud-based systems to mobile and …

A number of engineering and scientific problems require representing and manipulating
probability distributions over large alphabets, which we may think of as long vectors of reals …

Matrix multiplication is one of the bottleneck computations for training the weights within
deep neural networks. To speed up the training phase, we propose to use faster algorithms …

Information loss in numerical physics simulations can arise from various sources when
solving discretised partial differential equations. In particular, errors related to numerical …