OpenMENA: An Open-Source Memristor Interfacing and Compute Board for Neuromorphic Edge-AI Applications

TL;DR

OpenMENA is an open-source hardware and software platform that enables memristor-based in-memory computing for energy-efficient edge AI, supporting inference and learning on real devices with validated applications.

Contribution

It introduces the first fully open memristor interfacing system with hardware, firmware, and novel programming methods for practical edge-AI applications.

Findings

Validated on digit recognition tasks demonstrating effective weight transfer and adaptation.

Successfully applied to robot obstacle avoidance with memristor-based models.

Open source release to promote research in memristor-enabled edge AI.

Abstract

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.