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The paper introduces OpenMENA, an open-source memristor interfacing system designed for energy-efficient edge AI applications, featuring a reproducible hardware interface, a firmware-software stack with high-level APIs, and a Voltage-Incremental Proportional-Integral (VIPI) programming method. OpenMENA enables weight transfer and on-device adaptation by mitigating device non-idealities through chip-in-the-loop fine-tuning. The system's efficacy is demonstrated through digit recognition and a real-world robot obstacle-avoidance task, showcasing its ability to map localization inputs to motor commands.
Democratizing memristor-based edge AI, OpenMENA offers the first fully open-source system integrating hardware, firmware, and software for memristor crossbar interfacing and on-device learning.
Memristive crossbars enable in-memory multiply-accumulate and local plasticity learning, offering a path to energy-efficient edge AI. To this end, we present Open-MENA (Open Memristor-in-Memory Accelerator), which, to our knowledge, is the first fully open memristor interfacing system integrating (i) a reproducible hardware interface for memristor crossbars with mixed-signal read-program-verify loops; (ii) a firmware-software stack with high-level APIs for inference and on-device learning; and (iii) a Voltage-Incremental Proportional-Integral (VIPI) method to program pre-trained weights into analog conductances, followed by chip-in-the-loop fine-tuning to mitigate device non-idealities. OpenMENA is validated on digit recognition, demonstrating the flow from weight transfer to on-device adaptation, and on a real-world robot obstacle-avoidance task, where the memristor-based model learns to map localization inputs to motor commands. OpenMENA is released as open source to democratize memristor-enabled edge-AI research.