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This paper explores an energy-efficient resistive random access memory (RRAM) crossbar array framework for predicting epileptic seizures using the CHB-MIT electroencephalogram (EEG) dataset. RRAMs have significant potential for in-memory computing, offering a promising solution to overcome 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 

The Von Neumann bottleneck, a fundamental chal- lenge in conventional computer architecture, arises from the inability to execute fetch and data operations simultaneously due to a shared bus linking processing and memory units. This bottleneck significantly limits system performance, increases energy consumption, and exacerbates computational complex- ity. Emerging technologies such as Resistive Random Access Memories (RRAMs), leveraging crossbar arrays, offer promis- ing alternatives for addressing the demands of data-intensive computational tasks through in-memory computing of analog vector-matrix multiplication (VMM) operations. However, the propagation of errors due to device and circuit-level imperfec- tions remains a significant challenge. In this study, we introduce MELISO (In-Memory Linear Solver), a comprehensive end-to- end VMM benchmarking framework tailored for RRAM-based systems. MELISO evaluates the error propagation in VMM op- erations, analyzing the impact of RRAM device metrics on error magnitude and distribution. This paper introduces the MELISO framework and demonstrates its utility in characterizing and mitigating VMM error propagation using state-of-the-art RRAM device metrics. 

Resistive Random Access Memory (RRAM) devices hold promise as a key enabler technology for energy-efficient, in-memory, and brain-inspired computing paradigms, with the potential to significantly enhance high-performance computing applications. However, the widespread adoption of RRAM technology in high-performance computing applications is hindered by non-ideal device metrics and various reliability challenges. RRAM devices are reported to exhibit critical device-to-device (D2D) and cycle-to-cycle (C2C) variability. In this paper, we investigate D2D and C2C variabilities of Tantalum Oxide RRAM devices and explore potentiation, depression, and endurance dynamics under varying operation conditions. Our ultimate goal is to address performance and reliability issues associated with the oxide-based RRAM device technology and facilitate its broader implementation in future computing applications. 

Resistive Switching Random Access Memory (RRAM) technology is critical for advancing beyond von Neumann computing applications like neuromorphic computing. Enhancing RRAM performances is contingent on carefully controlling the properties of the switching layer material, such as composition, stoichiometry, and crystal structure. This paper reports the use of a Pulsed Laser Deposition (PLD) and post-growth annealing process to create TaOx films with different crystal structures, and their comprehensive characterization, including structural analysis using XRD and XPS techniques, as well as electrical characterization through I-V measurements to assess switching performance. Bipolar resistive switching dynamics is demonstrated for RRAM device stacks fabricated from both as-grown and annealed TaOx films. Additionally, electroformation, set, and reset voltage device metrics of RRAM devices are reported to increase as a result of the annealing process, which enhances the crystallization of the PLD-grown TaOx films.