Neuromorphic System Using Memcapacitors and Autonomous Local Learning
Neuromorphic System Using Memcapacitors and Autonomous Local Learning
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使用记忆电容的神经形态系统和自主局部学习
DOI：
10.1109/tnnls.2021.3106566
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发表时间：
2021-09
影响因子：
10.4
通讯作者：
Mutsumi Kimura;Yuma Ishisaki;Yuta Miyabe;Homare Yoshida;Isato Ogawa;Tomoharu Yokoyama;Ken-Ichi Haga-Ken-Ichi-H
中科院分区：
文献类型：
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作者：
Mutsumi Kimura;Yuma Ishisaki;Yuta Miyabe;Homare Yoshida;Isato Ogawa;Tomoharu Yokoyama;Ken-Ichi Haga-Ken-Ichi-H
人工智能有着广泛的应用，有望成为未来社会不可或缺的基础设施。神经网络是模仿人脑的代表性技术，具有多种优势。但体积庞大、功率巨大，而且由于是在诺依曼型计算机上执行，一些优点没有体现出来。神经形态系统是从硬件层面实现神经元和突触元件的仿生系统，尺寸紧凑、功耗低、运行鲁棒。然而，由于传统的硬件并非由完全优化的硬件组成，因此功率还不是最小的，并且必须使用额外的控制电路。在本文中，我们使用记忆电容器和自主局部学习开发了一个神经形态系统。通过使用记忆电容器，可以将功率降至最低，并且通过使用自主本地学习，可以删除处理突触元件的控制电路。首先，记忆电容器以横杆阵列形式完成，其中铁电层夹在水平和垂直电极之间。由于电介质极化，极化和电容表现出滞后现象。下面介绍自主局部学习。在训练阶段，直接发送要记忆的联想模式，施加相对高的电压，并诱发介电极化。在操作阶段，施加相对较低的电压，输入信号与记忆电容器的电容进行加权、求和并作为输出信号传输。最后搭建了实验系统并得到了实验结果。示出了训练阶段期间存储的模式、操作阶段期间作为输入信号的失真模式以及作为操作阶段期间输出信号的检索模式。研究人员发现，检索到的模式与记忆的模式完全相同。这意味着神经形态系统充当联想记忆。
Artificial intelligence is used for various applications and is promising as an indispensable infrastructure in future societies. Neural networks are representative technologies that imitate human brains and exhibit various advantages. However, the size is bulky, the power is huge, and some advantages are not demonstrated because they are executed on Neumann-type computers. Neuromorphic systems are biomimetic systems from the hardware level to implement neuron and synapse elements, and the size is compact, the power is low, and the operation is robust. However, because the conventional ones are not composed of fully optimized hardware, the power is not yet minimal, and extra control circuits must be used. In this article, we developed a neuromorphic system using memcapacitors and autonomous local learning. By using memcapacitors, the power can be minimal, and by using autonomous local learning, the control circuits to handle the synapse elements can be deleted. First, the memcapacitors are completed in a cross-bar array, where the ferroelectric layers are sandwiched between the horizontal and perpendicular electrodes. The polarization and capacitance exhibit hysteresis due to the dielectric polarization. Next, autonomous local learning is introduced as follows. During the training phase, associative patterns to be memorized are directly sent, relatively high voltages are applied, and dielectric polarizations are induced. During the operation phase, relatively low voltages are applied, and input signals are weighted with the capacitances of the memcapacitors, summed, and transferred as the output signals. Finally, the experimental system is set up, and the experimental results are acquired. The memorized patterns during the training phase, distorted patterns as the input signals during the operation phase, and retrieved patterns as the output signals in the operation phase are shown. Researchers found that the retrieved patterns are completely the same as the memorized patterns. This means that the neuromorphic system works as an associative memory.