Mannion Daniel J, Mehonic Adnan, Ng Wing H, Kenyon Anthony J
Department of Electronic and Electrical Engineering, University College London, London, United Kingdom.
Front Neurosci. 2020 Jan 17;13:1386. doi: 10.3389/fnins.2019.01386. eCollection 2019.
Memristors have many uses in machine learning and neuromorphic hardware. From memory elements in dot product engines to replicating both synapse and neuron wall behaviors, the memristor has proved a versatile component. Here we demonstrate an analog mode of operation observed in our silicon oxide memristors and apply this to the problem of edge detection. We demonstrate how a potential divider exploiting this analog behavior can prove a scalable solution to edge detection. We confirm its behavior experimentally and simulate its performance on a standard testbench. We show good performance comparable to existing memristor based work with a benchmark score of 0.465 on the BSDS500 dataset, while simultaneously maintaining a lower component count.
忆阻器在机器学习和神经形态硬件中有许多用途。从点积引擎中的存储元件到复制突触和神经元壁行为，忆阻器已被证明是一种多功能组件。在这里，我们展示了在我们的氧化硅忆阻器中观察到的一种模拟操作模式，并将其应用于边缘检测问题。我们展示了利用这种模拟行为的分压器如何能成为一种可扩展的边缘检测解决方案。我们通过实验证实了它的行为，并在标准测试平台上模拟了其性能。我们展示了良好的性能，在BSDS500数据集上的基准分数为0.465，与现有的基于忆阻器的工作相当，同时保持了较低的组件数量。
