Deep neural networks (DNNs) are widely used in various AI application domains such as
computer vision, natural language processing, autonomous driving, and bioinformatics. As …

The explosive expansion of Deep Neural Networks (DNN) model size expedites the need for
larger memory capacity. This movement is particularly true for models in natural language …

Computational storage devices enable in-storage processing of data in place. These
devices contain 64-bit application processors and hardware accelerators that can help …

Training deep neural network (DNN) models is a resource-intensive, iterative process. For
this reason, nowadays, complex optimizers like Adam are widely adopted as it increases the …

Deep neural networks (DNNs) with billion-scale parameters have demonstrated impressive
performance in solving many tasks. Unfortunately, training a billion-scale DNN is out of the …

Training convolutional neural networks (CNNs) requires intense computations and high
memory bandwidth. We find that bandwidth today is over-provisioned because most memory …

Deep neural networks (DNNs) have grown exponentially in size over the past decade,
leaving only those who have massive datacenter-based resources with the ability to develop …

Deep learning has been widely adopted for different applications of artificial intelligence-
speech recognition, natural language processing, computer vision etc. The growing size of …

In this paper, we present GradPIM, a processingin-memory architecture which accelerates
parameter updates of deep neural networks training. As one of processing-in-memory …

Dynamic neural network (DyNN) enables high computational efficiency and strong
representation capability. However, training DyNN can face a memory capacity problem …