Print Email Facebook Twitter Mapping-aware Biased Training for Accurate Memristor-based Neural Networks Title Mapping-aware Biased Training for Accurate Memristor-based Neural Networks Author Diware, S.S. (TU Delft Computer Engineering) Gebregiorgis, A.B. (TU Delft Computer Engineering) Joshi, R.V. (TU Delft Computer Engineering; IBM Research) Hamdioui, S. (TU Delft Quantum & Computer Engineering) Bishnoi, R.K. (TU Delft Computer Engineering) Department Quantum & Computer Engineering Date 2023 Abstract Memristor-based computation-in-memory (CIM) can achieve high energy efficiency by processing the data within the memory, which makes it well-suited for applications like neural networks. However, memristors suffer from conductance variation problem where their programmed conductance values deviate from the desired values. Such variations lead to computational errors that result in degraded inference accuracy in CIM-based neural networks. In this paper, we present a mapping-aware biased training methodology to mitigate the impact of conductance variation on CIM-based neural networks. We first determine which conductance states of the memristor are inherently more immune to variation. The neural network is then trained under the constraint that important weights can only take numeric values which directly get mapped to such favorable states. Simulation results show that our proposed mapping-aware biased training achieves up to 2.4× hardware accuracy compared to the conventional training. To reference this document use: http://resolver.tudelft.nl/uuid:4848cd17-7e1f-4ea4-9dd9-9d35f8b58f57 DOI https://doi.org/10.1109/AICAS57966.2023.10168661 Publisher IEEE Embargo date 2024-01-08 ISBN 9798350332674 Source AICAS 2023 - IEEE International Conference on Artificial Intelligence Circuits and Systems, Proceeding Event 5th IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2023, 2023-06-11 → 2023-06-13, Hangzhou, China Series AICAS 2023 - IEEE International Conference on Artificial Intelligence Circuits and Systems, Proceeding Bibliographical note Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. Part of collection Institutional Repository Document type conference paper Rights © 2023 S.S. Diware, A.B. Gebregiorgis, R.V. Joshi, S. Hamdioui, R.K. Bishnoi Files PDF Mapping_aware_Biased_Trai ... tworks.pdf 1.72 MB Close viewer /islandora/object/uuid:4848cd17-7e1f-4ea4-9dd9-9d35f8b58f57/datastream/OBJ/view