In the past decade, Graphics Processing Units have played an important role in the field of
high-performance computing and they still advance new fields such as IoT, autonomous …

Deep neural networks have proven to be particularly effective in visual and audio
recognition tasks. Existing models tend to be computationally expensive and memory …

State-of-the-art deep neural networks (DNNs) have hundreds of millions of connections and
are both computationally and memory intensive, making them difficult to deploy on …

We present the design and implementation of new inexact Newton type Bundle Adjustment
algorithms that exploit hardware parallelism for efficiently solving large scale 3D scene …

Deep learning systems have been successfully applied to Euclidean data such as images,
video, and audio. In many applications, however, information and their relationships are …

The memory coherence problem in designing and implementing a shared virtual memory on
loosely coupled multiprocessors is studied in depth. Two classes of algorithms, centralized …

Recent advances in computing have led to an explosion in the amount of data being
generated. Processing the ever-growing data in a timely manner has made throughput …

Convolutional neural networks (CNNs) are emerging as powerful tools for image
processing. Recent machine learning work has reduced CNNs' compute and data volumes …

Everyone who has seen the movie Wall Street will have seen a commercial security policy in
action. The recent work of Clark and Wilson and the WIPCIS initiative (the Workshop on …

Breadth-first search (BFS) is a core primitive for graph traversal and a basis for many higher-
level graph analysis algorithms. It is also representative of a class of parallel computations …