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.
|Title of host publication||AICAS 2023 - IEEE International Conference on Artificial Intelligence Circuits and Systems, Proceeding|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Publication status||Published - 2023|
|Event||5th IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2023 - Hangzhou, China|
Duration: 11 Jun 2023 → 13 Jun 2023
|Name||AICAS 2023 - IEEE International Conference on Artificial Intelligence Circuits and Systems, Proceeding|
|Conference||5th IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2023|
|Period||11/06/23 → 13/06/23|
Bibliographical noteGreen 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
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