开源硬件平台让类脑芯片在边缘设备上高效运行并自适应学习。
OpenMENA: An Open-Source Memristor Interfacing and Compute Board for Neuromorphic Edge-AI Applications
- 构建了可复现的忆阻器接口电路，支持读写验证闭环。
- 实现从预训练模型到硬件的权重迁移，并完成设备级微调。
- 适合想做类脑计算与边缘AI研究的开发者和科研人员。
忆阻器交叉阵列可实现存内乘加运算与局部可塑性学习，为节能型边缘人工智能提供新路径。本文提出OpenMENA（开放忆阻器存内加速器），据我们所知是首个完全开源的忆阻器接口系统，包含：(i) 可复现的忆阻器交叉阵列混合信号读-写-验证接口；(ii) 支持推理与设备端学习的软硬件栈及高级API；(iii) 一种电压增量比例积分（VIPI）方法，用于将预训练权重映射至模拟电导，并通过芯片在环微调缓解器件非理想性。OpenMENA在数字识别任务中验证了权重迁移至设备自适应的全流程，并在真实机器人避障任务中成功实现定位输入到电机指令的映射学习。该系统已开源，旨在推动忆阻器赋能边缘AI的研究普及。
原文摘要 · Abstract (English)
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.
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