title: Exploring the Hidden Dimension in Accelerating Convolutional Neural Networks
abstract: DeePa is a deep learning framework that explores parallelism in all parallelizable dimensions to accelerate the training process of convolutional neural networks. DeePa optimizes parallelism at the granularity of each individual layer in the network. We present an elimination-based algorithm that finds an optimal parallelism configuration for every layer. Our evaluation shows that DeePa achieves up to 6.5× speedup compared to state-of-the-art deep learning frameworks and reduces data transfers by up to 23×.
authors: Zhihao Jia Sina Lin Charles R. Qi Alex Aiken
hIndex: Alex Aiken 70