首页> 外文会议>2019 56th ACM/IEEE Design Automation Conference >Deep-DFR: A Memristive Deep Delayed Feedback Reservoir Computing System with Hybrid Neural Network Topology
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Deep-DFR: A Memristive Deep Delayed Feedback Reservoir Computing System with Hybrid Neural Network Topology

机译:Deep-DFR:具有混合神经网络拓扑的忆阻性深延迟反馈储层计算系统

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Deep neural networks (DNNs), the brain-like machine learning architecture, have gained immense success in data-extensive applications. In this work, a hybrid structured deep delayed feedback reservoir (Deep-DFR) computing model is proposed and fabricated. Our Deep-DFR employs memristive synapses working in a hierarchical information processing fashion with DFR modules as the readout layer, leading our proposed deep learning structure to be both depth-in-space and depth-in-time. Our fabricated prototype along with experimental results demonstrate its high energy efficiency with low hardware implementation cost. With applications on the image classification, MNIST and SVHN, our Deep-DFR yields a 1.26 $sim$ 7.69X reduction on the testing error compared to state-of-the-art DNN designs.
机译:深度神经网络(DNN)是类似于大脑的机器学习架构,已在数据广泛的应用中获得了巨大的成功。在这项工作中,提出并制造了一种混合结构的深延迟反馈储备库(Deep-DFR)计算模型。我们的Deep-DFR采用忆阻式突触,以DFR模块作为读出层,以分层信息处理方式工作,使我们提出的深度学习结构既具有空间深度,又具有时间深度。我们制造的原型以及实验结果证明了其高能效和较低的硬件实现成本。与最新的DNN设计相比,通过在图像分类,MNIST和SVHN上的应用,我们的Deep-DFR可使测试误差降低1.26 $ \\ sim $ 7.69X。

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