  Distributed training is a novel approach to accelerate Deep Neural Networks
(DNN) training, but common training libraries fall short of addressing the
distributed cases with heterogeneous processors or the cases where the
processing nodes get interrupted by other workloads. This paper describes
distributed training of DNN on computational storage devices (CSD), which are
NAND flash-based, high capacity data storage with internal processing engines.
A CSD-based distributed architecture incorporates the advantages of federated
learning in terms of performance scalability, resiliency, and data privacy by
eliminating the unnecessary data movement between the storage device and the
host processor. The paper also describes Stannis, a DNN training framework that
improves on the shortcomings of existing distributed training frameworks by
dynamically tuning the training hyperparameters in heterogeneous systems to
maintain the maximum overall processing speed in term of processed images per
second and energy efficiency. Experimental results on image classification
training benchmarks show up to 3.1x improvement in performance and 2.45x
reduction in energy consumption when using Stannis plus CSD compare to the
generic systems.
