CPU is a powerful, pervasive, and indispensable platform for running deep learning (DL)
workloads in systems ranging from mobile to extreme-end servers. In this article, we present …

Convolutional Neural Networks (CNN) are a class of Ar-tificial Neural Networks (ANN) that
are highly efficient at the pattern recognition tasks that underlie difficult AI prob-lems in a …

Deep Neural Networks (DNN) are used in a variety of applications and services. With the
evolving nature of DNNs, the race to build optimal hardware (both in datacenter and edge) …

Deep learning implementations on CPUs (Central Processing Units) are gaining more
traction. Enhanced AI capabilities on commodity x86 architectures are commercially …

We present Caffe con Troll (CcT), a fully compatible end-to-end version of the popular
framework Caffe with rebuilt internals. We built CcT to examine the performance …

The exponential growth in use of large deep neural networks has accelerated the need for
training these deep neural networks in hours or even minutes. This can only be achieved …

The rise of deep-learning (DL) has been fuelled by the improvements in accelerators. Due to
its unique features, the GPU continues to remain the most widely used accelerator for DL …

Deep learning, driven by large neural network models, is overtaking traditional machine
learning methods for understanding unstructured and perceptual data domains such as …

Abstract Support Vector Machines (SVM) have been widely used in data-mining and Big
Data applications as modern commercial databases start to attach an increasing importance …

Since its creation, the ImageNet-1k benchmark set has played a significant role as a
benchmark for ascertaining the accuracy of different deep neural net (DNN) models on the …