  Stochastic Gradient Descent (SGD) is the most popular algorithm for training
deep neural networks (DNNs). As larger networks and datasets cause longer
training times, training on distributed systems is common and distributed SGD
variants, mainly asynchronous and synchronous SGD, are widely used.
Asynchronous SGD is communication efficient but suffers from accuracy
degradation due to delayed parameter updating. Synchronous SGD becomes
communication intensive when the number of nodes increases regardless of its
advantage. To address these issues, we introduce Layered SGD (LSGD), a new
decentralized synchronous SGD algorithm. LSGD partitions computing resources
into subgroups that each contain a communication layer (communicator) and a
computation layer (worker). Each subgroup has centralized communication for
parameter updates while communication between subgroups is handled by
communicators. As a result, communication time is overlapped with I/O latency
of workers. The efficiency of the algorithm is tested by training a deep
network on the ImageNet classification task.
