Dataflow-Based Implementation of Deep Learning Application

Abstract

The proliferation of research on high efficient performance on deep learning has contributed to an increasing challenge and interest in the topic concerning the integration of this advanced-technology into daily life. Although a large amount of work on the domain of machine learning has been dedicated to the accuracy, efficiency, net topology and algorithm in the training and recognition procedures, the investigation of deep learning implementations in highly resource-constrainted contexts has been relatively unexplored due to the large computational requirements involved during the process of training large-scale network. In light of this, one process concentrated on parameters extraction and dataflow design, implementation, optimization of one deep learning application for vehicle classification on multicore platforms with limited numbers of available processor cores is demonstrated. By means of thousands of actors computation and fifos communication, we establish one enormous and complex dataflow graph, and then using the resulting dataflow representations, we apply a wide range of design optimizations to probe efficient implementations on three different multicore platforms. Through the incorporation of dataflow techniques, it is gratifying for us to see its effectiveness and efficiency in the several flexible experiments with alternative platforms that tailored to the resource constraints. Besides, we pioneer three general, novel, primitive and thorough flow charts during the work - deep leanring model, LIDE-C establishing model, LIDE-C coding model. Finally, not only LIDE-C we utilize for the implementation, but also DICE we apply for validation and verification. Both tools are incubated by DSPCAD at Maryland of University, and will be updated better in the future.