|Title||Energy efficient neural networks for big data analytics|
|Publication Type||Conference Paper|
|Year of Publication||2014|
|Authors||Y Wang, B Li, R Luo, Y Chen, N Xu, and H Yang|
|Conference Name||Proceedings Design, Automation and Test in Europe, Date|
The world is experiencing a data revolution to discover knowledge in big data. Large scale neural networks are one of the mainstream tools of big data analytics. Processing big data with large scale neural networks includes two phases: the training phase and the operation phase. Huge computing power is required to support the training phase. And the energy efficiency (power efficiency) is one of the major considerations of the operation phase. We first explore the computing power of GPUs for big data analytics and demonstrate an efficient GPU implementation of the training phase of large scale recurrent neural networks (RNNs). We then introduce a promising ultrahigh energy efficient implementation of neural networks' operation phase by taking advantage of the emerging memristor technique. Experiment results show that the proposed GPU implementation of RNNs is able to achieve 2 ∼ 11× speed-up compared with the basic CPU implementation. And the scaled-up recurrent neural network trained with GPUs realizes an accuracy of 47% on the Microsoft Research Sentence Completion Challenge, the best result achieved by a single RNN on the same dataset. In addition, the proposed memristor-based implementation of neural networks demonstrates power efficiency of > 400 GFLOPS/W and achieves energy savings of 22× on the HMAX model compared with its pure digital implementation counterpart. © 2014 EDAA.