Abstract
Due to the inevitability of the IR drop problem in passive ReRAM crossbar arrays, finding a software solution that can predict the effect of IR drop without the need of expensive SPICE simulations, is very desirable. In this paper, two simple neural networks are proposed as software solution to predict the effect of IR drop. These networks can be easily integrated in any deep neural network framework to incorporate the IR drop problem during training. As an example, the proposed solution is integrated in BinaryNet framework and the test validation results, done through SPICE simulations, show very high improvement in performance close to the baseline performance, which demonstrates the efficacy of the proposed method. In addition, the proposed solution outperforms the prior work on challenging datasets such as CIFAR10 and SVHN.
Original language | English (US) |
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Title of host publication | 2020 57th ACM/IEEE Design Automation Conference, DAC 2020 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781450367257 |
DOIs | |
State | Published - Jul 2020 |
Event | 57th ACM/IEEE Design Automation Conference, DAC 2020 - Virtual, San Francisco, United States Duration: Jul 20 2020 → Jul 24 2020 |
Publication series
Name | Proceedings - Design Automation Conference |
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Volume | 2020-July |
ISSN (Print) | 0738-100X |
Conference
Conference | 57th ACM/IEEE Design Automation Conference, DAC 2020 |
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Country/Territory | United States |
City | Virtual, San Francisco |
Period | 07/20/20 → 07/24/20 |
Bibliographical note
Publisher Copyright:© 2020 IEEE.
Keywords
- 0T1R
- Binary neural network
- Deep neural network
- IR drop
- ReRAM crossbar array
ASJC Scopus subject areas
- Computer Science Applications
- Control and Systems Engineering
- Electrical and Electronic Engineering
- Modeling and Simulation