Dynamic Detection and Mitigation of Read-disturb for Accurate Memristor-based Neural Networks
Sumit Diware (TU Delft - Computer Engineering)
Mohammad Amin Yaldagard (TU Delft - Computer Engineering)
Anteneh Gebregiorgis (TU Delft - Computer Engineering)
Rajiv V. Joshi (TU Delft - Computer Engineering)
Said Hamdioui (TU Delft - Computer Engineering)
Rajendra Bishnoi (TU Delft - Computer Engineering)
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Abstract
Computation-in-memory (CIM) using memristors can facilitate data processing within the memory itself, leading to superior energy efficiency than conventional von-Neumann architecture. This makes CIM well-suited for data-intensive applications like neural networks. However, a large number of read operations can induce an undesired resistance change in the memristor, known as read-disturb. As memristor resistances represent the neural network weights in CIM hardware, read-disturb causes an unintended change in the network’s weights that leads to poor accuracy. In this paper, we propose a methodology for read-disturb detection and mitigation in CIM-based neural networks. We first analyze the key insights regarding the read-disturb phenomenon. We then introduce a mechanism to dynamically detect the occurrence of read-disturb in CIM-based neural networks. In response to such detections, we develop a method that adapts the sensing conditions of CIM hardware to provide error-free operation even in the presence of read-disturb. Simulation results show that our proposed methodology achieves up to 2× accuracy and up to 2× correct operations per unit energy compared to conventional CIM architectures.