17285

MobiRNN: Efficient Recurrent Neural Network Execution on Mobile GPU

Qingqing Cao, Niranjan Balasubramanian, Aruna Balasubramanian
Stony Brook University
arXiv:1706.00878 [cs.DC], (3 Jun 2017)

@article{cao2017mobirnn,

   title={MobiRNN: Efficient Recurrent Neural Network Execution on Mobile GPU},

   author={Cao, Qingqing and Balasubramanian, Niranjan and Balasubramanian, Aruna},

   year={2017},

   month={jun},

   archivePrefix={"arXiv"},

   primaryClass={cs.DC}

}

In this paper, we explore optimizations to run Recurrent Neural Network (RNN) models locally on mobile devices. RNN models are widely used for Natural Language Processing, Machine Translation, and other tasks. However, existing mobile applications that use RNN models do so on the cloud. To address privacy and efficiency concerns, we show how RNN models can be run locally on mobile devices. Existing work on porting deep learning models to mobile devices focus on Convolution Neural Networks (CNNs) and cannot be applied directly to RNN models. In response, we present MobiRNN, a mobile-specific optimization framework that implements GPU offloading specifically for mobile GPUs. Evaluations using an RNN model for activity recognition shows that MobiRNN does significantly decrease the latency of running RNN models on phones.
VN:F [1.9.22_1171]
Rating: 3.0/5 (2 votes cast)
MobiRNN: Efficient Recurrent Neural Network Execution on Mobile GPU, 3.0 out of 5 based on 2 ratings

* * *

* * *

HGPU group © 2010-2017 hgpu.org

All rights belong to the respective authors

Contact us: