6 research outputs found
Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis
Deep Neural Networks (DNNs) are becoming an important tool in modern
computing applications. Accelerating their training is a major challenge and
techniques range from distributed algorithms to low-level circuit design. In
this survey, we describe the problem from a theoretical perspective, followed
by approaches for its parallelization. We present trends in DNN architectures
and the resulting implications on parallelization strategies. We then review
and model the different types of concurrency in DNNs: from the single operator,
through parallelism in network inference and training, to distributed deep
learning. We discuss asynchronous stochastic optimization, distributed system
architectures, communication schemes, and neural architecture search. Based on
those approaches, we extrapolate potential directions for parallelism in deep
learning
Brain-Inspired Computing
This open access book constitutes revised selected papers from the 4th International Workshop on Brain-Inspired Computing, BrainComp 2019, held in Cetraro, Italy, in July 2019. The 11 papers presented in this volume were carefully reviewed and selected for inclusion in this book. They deal with research on brain atlasing, multi-scale models and simulation, HPC and data infra-structures for neuroscience as well as artificial and natural neural architectures
XXIII Congreso Argentino de Ciencias de la Computaci贸n - CACIC 2017 : Libro de actas
Trabajos presentados en el XXIII Congreso Argentino de Ciencias de la Computaci贸n (CACIC), celebrado en la ciudad de La Plata los d铆as 9 al 13 de octubre de 2017, organizado por la Red de Universidades con Carreras en Inform谩tica (RedUNCI) y la Facultad de Inform谩tica de la Universidad Nacional de La Plata (UNLP).Red de Universidades con Carreras en Inform谩tica (RedUNCI