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Memristor-based crossbar arrays represent a promising emerging memorytechnology to replace conventional memories by offering a high density andenabling computing-in-memory (CIM) paradigms. While analog computing providesthe best performance, non-idealities and ADC/DAC conversion limitmemristor-based CIM. Logic-in-Memory (LIM) presents another flavor of CIM, inwhich the memristors are used in a binary manner to implement logic gates.Since binary neural networks (BNNs) use binary logic gates as the dominantoperation, they can benefit from the massively parallel execution of binaryoperations and better resilience to variations of the memristors. Althoughconventional neural networks have been thoroughly investigated, the impact offaults on memristor-based BNNs remains unclear. Therefore, we analyze theimpact of faults on logic gates in memristor-based crossbar arrays for BNNs. Wepropose a simulation framework that simulates different traditional faults toexamine the accuracy loss of BNNs on memristive crossbar arrays. In addition,we compare different logic families based on the robustness and feasibility toaccelerate AI applications.

X-Fault: Impact of Faults on Binary Neural Networks in Memristor-Crossbar Arrays with Logic-in-Memory Computation