Memristors have shown promising features for enhancing neuromorphic computing concepts and AI hardware accelerators. In this paper, we present a user-friendly software infrastructure that allows emulating a wide range of neuromorphic architectures with memristor models. This tool empowers studies that exploit memristors for online learning and online classification tasks, predicting memristor resistive state changes during the training process. The versatility of the tool is showcased through the capability for users to customise parameters in the employed memristor and neuronal models as well as the employed learning rules. This further allows users to validate concepts and their sensitivity across a wide range of parameters. We demonstrate the use of the tool via an MNIST classification task. Finally, we show how this tool can also be used to emulate the concepts under study in-silico with practical memristive devices via appropriate interfacing with commercially available characterisation tools.
翻译:模拟器展示了加强神经形态计算概念和 AI 硬件加速器的有希望的功能。 在本文中, 我们展示了一种方便用户的软件基础设施, 使得能够用分子模型模拟一系列广泛的神经形态结构。 这个工具授权研究利用分子进行在线学习和在线分类任务, 预测培训过程中的分子抵抗状态变化。 该工具的多功能性通过用户在雇用的分子模型和神经模型中定制参数的能力以及应用的学习规则来展示。 这进一步允许用户在广泛的参数中验证各种概念及其敏感性。 我们通过MMIST分类任务演示了该工具的使用。 最后, 我们展示了该工具如何通过与商业上可用的性能工具进行适当的互动, 以实用的记忆设备在硅研究中模仿这些概念。