Deep Neural Networks (DNNs) surpass the human-level performance on specific tasks. The outperforming capability accelerate an adoption of DNNs to safety-critical applications such as autonomous vehicles and medical diagnosis. Millions of parameters in DNN requires a high memory capacity. A process technology scaling allows increasing memory density, however, the memory reliability confronts significant reliability issues causing errors in the memory. This can make stored weights in memory erroneous. Studies show that the erroneous weights can cause a significant accuracy loss. This motivates research on fault-tolerant DNN architectures. Despite of these efforts, DNNs are still vulnerable to errors, especially error in DNN classifier. In the worst case, because a classifier in convolutional neural network (CNN) is the last stage determining an input class, a single error in the classifier can cause a significant accuracy drop. To enhance the fault tolerance in CNN, this paper proposes a novel bipolar vector classifier which can be easily integrated with any CNN structures and can be incorporated with other fault tolerance approaches. Experimental results show that the proposed method stably maintains an accuracy with a high bit error rate up to 10-3 in the classifier.
|Title of host publication||Proceedings of the 59th ACM/IEEE Design Automation Conference, DAC 2022|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|Publication status||Published - 2022 Jul 10|
|Event||59th ACM/IEEE Design Automation Conference, DAC 2022 - San Francisco, United States|
Duration: 2022 Jul 10 → 2022 Jul 14
|Name||Proceedings - Design Automation Conference|
|Conference||59th ACM/IEEE Design Automation Conference, DAC 2022|
|Period||22/7/10 → 22/7/14|
Bibliographical noteFunding Information:
This work was supported in part by the Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education under Grant NRF-2018R1D1A1B07049842; in part by the Institute of Information Communications Technology Planning Evaluation (IITP) grant funded by the Korea Government (MSIT) under Grant 2021-0-00106; in part by the Next-Generation Intelligent Semiconductor Development by the Ministry of Trade, Industry and Energy (MOTIE) under Grant 20011074; and in part by the 2020 Yonsei University Future-Leading Research Initiative.
© 2022 ACM.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Control and Systems Engineering
- Electrical and Electronic Engineering
- Modelling and Simulation