论文标题
在卷积神经网络中，耦合的VO2振荡器电路作为模拟第一层滤波器
Coupled VO2 oscillators circuit as analog first layer filter in convolutional neural networks
论文作者
论文摘要
在这项工作中，我们提出了一个基于硅横杆配置中构造的耦合VO2振荡器的内存计算平台。与现有平台相比，横梁配置有望在面积密度和振荡频率方面有显着改善。此外，横梁设备的可变性较低和扩展可靠性，因此可以在4耦合振荡器上进行实验。我们使用振荡器的相位关系证明了神经形态计算能力。作为应用程序，我们建议用振荡电路替换卷积神经网络中的数字过滤操作。该概念通过MNIST数据集的VGG13体系结构进行了测试，在识别任务中实现了95％的表现。
In this work we present an in-memory computing platform based on coupled VO2 oscillators fabricated in a crossbar configuration on silicon. Compared to existing platforms, the crossbar configuration promises significant improvements in terms of area density and oscillation frequency. Further, the crossbar devices exhibit low variability and extended reliability, hence, enabling experiments on 4-coupled oscillator. We demonstrate the neuromorphic computing capabilities using the phase relation of the oscillators. As a application, we propose to replace digital filtering operation in a convolutional neural network with oscillating circuits. The concept is tested with a VGG13 architecture on the MNIST dataset, achieving performances of 95% in the recognition task.
