SOLAR: Services-oriented Deep Learning Architectures

Abstract

Deep learning has been an emerging field of machine learning during past decades. However, the diversity and large scale data size have posed significant challenge to construct a flexible and high performance implementations of deep learning neural networks. In order to improve the performance as well to maintain the scalability, in this paper we present SOLAR, a services-oriented deep learning architecture using various accelerators like GPU and FPGA. SOLAR provides a uniform programming model to users so that the hardware implementation and the scheduling is invisible to the programmers. At runtime, the services can be executed either on the software processors or the hardware accelerators. To leverage the trade-offs between the metrics among performance, power, energy, and efficiency, we present a multitarget design space exploration. Experimental results on the real state-of-the-art FPGA board demonstrate that the SOLAR is able to provide a ubiquitous framework for diverse applications without increasing the burden of the programmers. Moreover, the speedup of the GPU and FPGA hardware accelerator in SOLAR can achieve significant speedup comparing to the conventional Intel i5 processors with great scalability.