Transient Variability in SOI-Based LIF Neuron and Impact on Unsupervised Learning
Transient Variability in SOI-Based LIF Neuron and Impact on Unsupervised Learning
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基于 SOI 的 LIF 神经元的瞬态变异及其对无监督学习的影响
DOI：
10.1109/ted.2018.2872407
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发表时间：
2018
影响因子：
3.1
通讯作者：
U. Ganguly
中科院分区：
文献类型：
--
作者：
S. Dutta;Tinish Bhattacharya;N. Mohapatra;Manan Suri;U. Ganguly
变异性是生物学不可或缺的一部分。尽管存在变异性，生物神经网络仍能有效地执行，有时其性能会因变异性而得到促进。因此，研究其电子模拟物的变异性对于构建仿生神经网络是必不可少的。我们最近展示了一个紧凑的漏积分和火灾（LIF）神经元上的PD-绝缘体上硅（SOI）MOSFET。在本文中，我们研究了碰撞电离（II）引起的变化，设备到设备（D2 D）和周期到周期（C2C）的SOI神经元。C2C的可变性归因于II产生的电荷存储的波动，并且与无II的情况相比，其增强了至少2.5倍。另一方面，D2 D变异性与II诱导的锐阈下斜率（~40 mV/decade）相关，与无II病例相比，其变异性增强了$\sim 20\times $。SOI神经元中增强的可变性对无监督分类任务的影响通过模拟具有模拟突触和二进制突触的尖峰神经网络（SNN）来评估。对于基于模拟突触的SNN，C2C可变性相对于理想LIF神经元将性能提高了约5%。然而，D2 D的可变性，以及D2 D和C2C的组合可变性，使学习性能下降了-10%。对于二进制突触，我们观察到，性能急剧下降的理想LIF神经元的突触权重初始化成为非随机的。然而，具有实验证明的变异性（C2C和D2 D）的神经元减轻了这一挑战。因此，这使得二进制突触能够与模拟突触同等地执行，这允许确定性权重初始化。这使得用于随机权重初始化的RNG电路冗余。
Variability is an integral part of biology. A biological neural network performs efficiently despite variability and sometimes its performance is facilitated by the variability. Hence, the study of variability on its electronic analog is essential for constructing biomimetic neural networks. We have recently demonstrated a compact leaky integrate and fire (LIF) neuron on PD-silicon on insulator (SOI) MOSFET. In this paper, we have studied impact ionization (II)-induced variability both device-to-device (D2D) and cycle-to-cycle (C2C) in the SOI neuron. The C2C variability is attributed to the fluctuation in the II-generated charge storage and it is enhanced by at least $2.5\times $ as compared to the no-II case. The D2D variability, on the other hand, is related to the II-induced sharp subthreshold slope (~40 mV/decade), which enhanced the variability by $\sim 20\times $ compared to the no-II case. The impact of the enhanced variability in SOI neurons on an unsupervised classification task was evaluated by simulating a spiking neural network (SNN) with both analog and binary synapses. For analog synapse-based SNN, the C2C variability improved the performance by ~5% relative to ideal LIF neurons. However, the D2D variability, as well as combined D2D and C2C variability, degrades learning by −~10%. For binary synapses, we observe that performance drastically degrades for ideal LIF neurons as the synaptic weight initialization becomes nonrandom. However, neurons with the experimentally demonstrated variability (C2C and D2D) mitigate this challenge. Therefore, this enables binary synapses to perform at par with analog synapses, which allows for deterministic weight initialization. This makes RNG circuits for random weight initialization redundant.
影响因子：
3.1
作者：
Suri, Manan;Querlioz, Damien;DeSalvo, Barbara
通讯作者：
DeSalvo, Barbara
影响因子：
38.3
作者：
Tuma, Tomas;Pantazi, Angeliki;Eleftheriou, Evangelos
通讯作者：
Eleftheriou, Evangelos