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The recent popularity of deep neural networks (DNNs) has generated a lot ofresearch interest in performing DNN-related computation efficiently. However,the primary focus is usually very narrow and limited to (i) inference -- i.e.how to efficiently execute already trained models and (ii) image classificationnetworks as the primary benchmark for evaluation.  Our primary goal in this work is to break this myopic view by (i) proposing anew benchmark for DNN training, called TBD (TBD is short for Training Benchmarkfor DNNs), that uses a representative set of DNN models that cover a wide rangeof machine learning applications: image classification, machine translation,speech recognition, object detection, adversarial networks, reinforcementlearning, and (ii) by performing an extensive performance analysis of trainingthese different applications on three major deep learning frameworks(TensorFlow, MXNet, CNTK) across different hardware configurations (single-GPU,multi-GPU, and multi-machine). TBD currently covers six major applicationdomains and eight different state-of-the-art models.  We present a new toolchain for performance analysis for these models thatcombines the targeted usage of existing performance analysis tools, carefulselection of new and existing metrics and methodologies to analyze the results,and utilization of domain specific characteristics of DNN training. We alsobuild a new set of tools for memory profiling in all three major frameworks;much needed tools that can finally shed some light on precisely how much memoryis consumed by different data structures (weights, activations, gradients,workspace) in DNN training. By using our tools and methodologies, we makeseveral important observations and recommendations on where the future researchand optimization of DNN training should be focused.

TBD: Benchmarking and Analyzing Deep Neural Network Training