  Real-time artificial intelligence (AI) applications mapped onto edge
computing need to perform data capture, process data, and device actuation
within given bounds while using the available devices. Task synchronization
across the devices is an important problem that affects the timely progress of
an AI application by determining the quality of the captured data, time to
process the data, and the quality of actuation. In this paper, we develop a
fast edge-based synchronization scheme that can time align the execution of
input-output tasks as well compute tasks. The primary idea of the fast
synchronizer is to cluster the devices into groups that are highly synchronized
in their task executions and statically determine few synchronization points
using a game-theoretic solver. The cluster of devices use a late notification
protocol to select the best point among the pre-computed synchronization points
to reach a time aligned task execution as quickly as possible. We evaluate the
performance of our synchronization scheme using trace-driven simulations and we
compare the performance with existing distributed synchronization schemes for
real-time AI application tasks. We implement our synchronization scheme and
compare its training accuracy and training time with other parameter server
synchronization frameworks.
