Choi Kyu-Bong, Woo Sung Yun, Kang Won-Mook, Lee Soochang, Kim Chul-Heung, Bae Jong-Ho, Lim Suhwan, Lee Jong-Ho
Department of Electrical and Computer Engineering, Inter-University Semiconductor Research Center, Seoul National University, Seoul, South Korea.
Front Neurosci. 2018 Oct 9;12:704. doi: 10.3389/fnins.2018.00704. eCollection 2018.
Hardware-based spiking neural networks (SNNs) to mimic biological neurons have been reported. However, conventional neuron circuits in SNNs have a large area and high power consumption. In this work, a split-gate floating-body positive feedback (PF) device with a charge trapping capability is proposed as a new neuron device that imitates the integrate-and-fire function. Because of the PF characteristic, the subthreshold swing () of the device is less than 0.04 mV/dec. The super-steep of the device leads to a low energy consumption of ∼0.25 pJ/spike for a neuron circuit (PF neuron) with the PF device, which is ∼100 times smaller than that of a conventional neuron circuit. The charge storage properties of the device mimic the integrate function of biological neurons without a large membrane capacitor, reducing the PF neuron area by about 17 times compared to that of a conventional neuron. We demonstrate the successful operation of a dense multiple PF neuron system with reset and lateral inhibition using a common self-controller in a neuron layer through simulation. With the multiple PF neuron system and the synapse array, on-line unsupervised pattern learning and recognition are successfully performed to demonstrate the feasibility of our PF device in a neural network.
据报道，已出现用于模仿生物神经元的基于硬件的脉冲神经网络（SNN）。然而，SNN中的传统神经元电路面积大且功耗高。在这项工作中，提出了一种具有电荷俘获能力的分裂栅浮体正反馈（PF）器件，作为一种模仿积分发放功能的新型神经元器件。由于PF特性，该器件的亚阈值摆幅（）小于0.04 mV/dec。该器件超陡的亚阈值摆幅使得采用PF器件的神经元电路（PF神经元）的能耗低至约0.25 pJ/脉冲，比传统神经元电路小约100倍。该器件的电荷存储特性模仿了生物神经元的积分功能，无需大型膜电容，与传统神经元相比，PF神经元的面积减小了约17倍。我们通过仿真证明了在神经元层中使用通用自控制器的具有复位和侧向抑制功能的密集多PF神经元系统的成功运行。利用多PF神经元系统和突触阵列，成功地进行了在线无监督模式学习和识别，以证明我们的PF器件在神经网络中的可行性。
