As the number of cores on a chip increases and key applications become even more data-
intensive, memory systems in modern processors have to deal with increasingly large …

As one of the most promising energy-efficient computing paradigms, approximate computing
has gained a lot of research attention in the past few years. This paper presents a survey of …

Approximate computing has gained research attention recently as a way to increase energy
efficiency and/or performance by exploiting some applications' intrinsic error resiliency …

General-purpose GPUs (GPGPUs) are becoming prevalent in mainstream computing, and
performance per watt has emerged as a more crucial evaluation metric than peak …

Popular deep learning frameworks require users to fine-tune their memory usage so that the
training data of a deep neural network (DNN) fits within the GPU physical memory. Prior …

Data compression is a promising approach for meeting the increasing memory capacity
demands expected in future systems. Unfortunately, existing compression algorithms do not …

This paper presents Warped-Compression, a warp-level register compression scheme for
reducing GPU power consumption. This work is motivated by the observation that the …

In this paper we propose a technique to adapt a convolutional neural network (CNN) based
object counter to additional visual domains and object types while still preserving the …

Modern discrete GPUs support unified memory and demand paging. Automatic
management of data movement between CPU memory and GPU memory dramatically …

Modern Graphics Processing Units (GPUs) are well provisioned to support the concurrent
execution of thousands of threads. Unfortunately, different bottlenecks during execution and …