OpenMENA: An Open-Source Memristor Interfacing and Compute Board for Neuromorphic Edge-AI Applications
This work addresses the problem of democratizing memristor-based edge-AI research for developers and researchers by providing an open-source platform, though it is incremental in building upon existing memristor technology.
The authors tackled the challenge of enabling energy-efficient edge AI by developing OpenMENA, the first fully open-source memristor interfacing system, which demonstrated successful digit recognition and robot obstacle-avoidance tasks with 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.