The Cloud computing paradigm has revolutionised the computer science horizon during the
past decade and has enabled the emergence of computing as the fifth utility. It has captured …

Computational biology and bioinformatics provide vast data gold-mines from protein
sequences, ideal for Language Models (LMs) taken from Natural Language Processing …

Computational biology and bioinformatics provide vast data gold-mines from protein
sequences, ideal for Language Models taken from NLP. These LMs reach for new prediction …

The most natural way to speed-up the training of large networks is to use data-parallelism on
multiple GPUs. To scale Stochastic Gradient (SG) based methods to more processors, one …

(Frankle & Carbin, 2019) shows that there exist winning tickets (small but critical
subnetworks) for dense, randomly initialized networks, that can be trained alone to achieve …

Initially developed for gaming and 3-D rendering, graphics processing units (GPUs) were
recognized to be a good fit to accelerate deep learning training. Its simple mathematical …

State-of-the-art deep learning systems rely on iterative distributed training to tackle the
increasing complexity of models and input data. In this work, we identify an opportunity for …

Highly distributed training of Deep Neural Networks (DNNs) on future compute platforms
(offering 100 of TeraOps/s of computational capacity) is expected to be severely …

Convolution layers are prevalent in many classes of deep neural networks, including
Convolutional Neural Networks (CNNs) which provide state-of-the-art results for tasks like …

Convolutional neural networks (CNNs) have been increasingly deployed to edge devices.
Hence, many efforts have been made towards efficient CNN inference on resource …