Low Power Memristor Crossbar Based Winner Takes All Circuit
Low Power Memristor Crossbar Based Winner Takes All Circuit
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DOI：
10.1109/ijcnn.2018.8489735
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发表时间：
2018-07
期刊：
影响因子：
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通讯作者：
Rasitha Fernando;Raqibul Hasan;T. Taha
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文献类型：
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作者：
Rasitha Fernando;Raqibul Hasan;T. Taha
边缘设备通常必须以低功耗处理数据，并将从适应性中受益。鉴于进入这些设备的数据通常是无标签的，因此在这些设备上进行无人监督的培训是有益的。本文研究了一种低功耗的方法来实现Winner Take All算法，即通过基于忆阻器交叉开关的电路自组织映射。设计了一种新颖的神经元电路，用于获胜神经元的检测和侧抑制操作。实验结果表明，该系统能够基于未标注的训练数据进行自组织。建议的设计约为0.002 mm$^{\mathbf{2\，$extbf{{面积，功耗约0.2 mW。与CPU相比，该设计具有更高的错误率，但速度是CPU的100倍，并且消耗的面积和功耗要低得多。因此，当面积或功率的减少至关重要时，这种方法是相当可行的。
Edge devices often have to processdata at low power and would benefit from being adaptable. Given that the data coming into these devices is generally unlabeled, unsupervised training on these devices is beneficial. This paper examines a low power approach to implement the winner takes all algorithm, for self-organizing maps through a memristor crossbar based circuit. A novel neuron circuit is designed for the winning neuron detection and lateral inhibition operations. Our experimental results show that the proposed system can self-organize based on unlabeled training data. The proposed design was around 0.002mm$^{\mathbf{2\, $textbf{{in area and consumed about 0.2mW of power. When compared to a CPU, the design had a higher error rate, but was 100 times faster and consumed much lower area and power. Thus when area or power reduction are crucially important, this approach is quite viable.