ABSTRACT Batch processing of dynamic graphs is a very common technique for a variety of applications, such as computer vision and natural language processing. However, due to the varieties of type and ...

NeuGraph introduces graph computation optimizations into the management of data partitioning, scheduling, and parallelism in dataflow-based deep learning frameworks. Our evaluation shows that, on ...

Which are the segmentation algorithms proposed during 2018-2019 in CVPR that have CNN architecture?’ Answering this question involves identifying and analyzing the deep learning architecture diagrams ...

Graphs, a potentially extensive web of nodes connected by edges, can be used to express and interrogate relationships between data, like social connections, financial transactions, traffic, energy ...

Deep learning at the speed of light. ... We compile these ops into complex GPU kernels, so even though our ops are simple, we get high performance through the power of compilers! ... All neural ...

Over the past few years, deep learning has been rapidly adopted in many fields. Among the various hardware accelerators specifically for deep learning computation, graphics processing units (GPUs) are ...

Which are the segmentation algorithms proposed during 2018-2019 in CVPR that have CNN architecture?’ Answering this question involves identifying and analyzing the deep learning architecture diagrams ...