Recently, the researches on DNN (deep neural network) using GPUs has been actively conducted. The reason for using GPUs in DNN is that it reduces the learning time by using many computational cores. However, GPUs have no implements to support the reliable computing operations. It can exacerbate the reliability of the deep neural network. To ensure the reliability of the deep neural network, the proposed approach is to utilize the dropout technique used in MLP (Multi-Layer Perceptron) learning. In case of the dropout method, some threads in GPUs do not participate in the calculation and it causes GPUs underutilization. The proposed method uses the GPUs underutilization to support a reliable deep neural network. The proposed approach is available through using the idle neurons with adjacent calculating neurons in the dropout process. The experiment results show that the proposed approach is able to support the reliability issues in GPUs while executing deep neural network algorithms.
|Title of host publication||Proceedings of TENCON 2018 - 2018 IEEE Region 10 Conference|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|Publication status||Published - 2019 Feb 22|
|Event||2018 IEEE Region 10 Conference, TENCON 2018 - Jeju, Korea, Republic of|
Duration: 2018 Oct 28 → 2018 Oct 31
|Name||IEEE Region 10 Annual International Conference, Proceedings/TENCON|
|Conference||2018 IEEE Region 10 Conference, TENCON 2018|
|Country||Korea, Republic of|
|Period||18/10/28 → 18/10/31|
Bibliographical noteFunding Information:
This work was supported by Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIP) (No.2016-0-00140, Development of Application Program Optimization Tools for High Performance Computing Systems).
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Electrical and Electronic Engineering