1 research outputs found
Bridge Data Center AI Systems with Edge Computing for Actionable Information Retrieval
Extremely high data rates at modern synchrotron and X-ray free-electron laser
light source beamlines motivate the use of machine learning methods for data
reduction, feature detection, and other purposes. Regardless of the
application, the basic concept is the same: data collected in early stages of
an experiment, data from past similar experiments, and/or data simulated for
the upcoming experiment are used to train machine learning models that, in
effect, learn specific characteristics of those data; these models are then
used to process subsequent data more efficiently than would general-purpose
models that lack knowledge of the specific dataset or data class. Thus, a key
challenge is to be able to train models with sufficient rapidity that they can
be deployed and used within useful timescales. We describe here how specialized
data center AI (DCAI) systems can be used for this purpose through a
geographically distributed workflow. Experiments show that although there are
data movement cost and service overhead to use remote DCAI systems for DNN
training, the turnaround time is still less than 1/30 of using a locally
deploy-able GPU