Benchmarking RRAM Crossbar Arrays for Epileptic Seizure Prediction

This paper explores an energy-efficient resistive random access memory (RRAM) crossbar array framework for predicting epileptic seizures using the CUB-MIT electroen-cephalogram (EEG) dataset. RRAMs have significant potential for in-memory computing, offering a promising solution to over-come the limitations of the traditional Von Neumann architecture. By integrating a domain-specific feature extraction approach and evaluating the optimal RRAM hardware parameters using the NeuroSim+ benchmarking platform, we assess the performance of RRAM crossbars for predicting epileptic seizures. Our proposed workflow achieves accuracy levels above 80% despite the EEG data being quantized to 1-bit, highlighting the robustness and efficiency of our approach for epileptic seizure prediction.