Effective Approaches to Batch Parallelization for Dynamic Neural Network Architectures

Autor:	Suarez, Joseph, Zhu, Clare
Rok vydání:	2017
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	We present a simple dynamic batching approach applicable to a large class of dynamic architectures that consistently yields speedups of over 10x. We provide performance bounds when the architecture is not known a priori and a stronger bound in the special case where the architecture is a predetermined balanced tree. We evaluate our approach on Johnson et al.'s recent visual question answering (VQA) result of his CLEVR dataset by Inferring and Executing Programs (IEP). We also evaluate on sparsely gated mixture of experts layers and achieve speedups of up to 1000x over the naive implementation. Comment: Code at https://github.com/jsuarez5341/Efficient-Dynamic-Batching
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1707.02402 Zobrazit plný text záznamu View this record from Arxiv