BaPipe: Exploration of Balanced Pipeline Parallelism for DNN Training

The size of deep neural networks (DNNs) grows rapidly as the complexity of the machine learning algorithm increases. To satisfy the requirement of computation and memory of DNN training, distributed deep learning based on model parallelism has been widely recognized. We propose a new pipeline parallelism training framework, BaPipe, which can automatically explore pipeline parallelism training methods and balanced partition strategies for DNN distributed training. In BaPipe, each accelerator calculates the forward propagation and backward propagation of different parts of networks to implement the intra-batch pipeline parallelism strategy. BaPipe uses a new load balancing automatic exploration strategy that considers the parameters of DNN models and the computation, memory, and communication resources of accelerator clusters. We have trained different DNNs such as VGG-16, ResNet-50, and GNMT on GPU clusters and simulated the performance of different FPGA clusters. Compared with state-of-the-art data parallelism and pipeline parallelism frameworks, BaPipe provides up to 3.2x speedup and 4x memory reduction in various platforms.

Seeing Neural Networks Through a Box of Toys: The Toybox Dataset of Visual Object Transformations

Deep convolutional neural networks (CNNs) have enjoyed tremendous success in computer vision in the past several years, particularly for visual object recognition.However, how CNNs work remains poorly understood, and the training of deep CNNs is still considered more art than science. To better characterize deep CNNs and the training process, we introduce a new video dataset called Toybox. Images in Toybox come from first-person, wearable camera recordings of common household objects and toys being manually manipulated to undergo structured transformations like rotations and translations. We also present results from initial experiments using deep CNNs that begin to examine how different distributions of training data can affect visual object recognition performance, and how visual object concepts are represented within a trained network.