Neuro-biology inspired Spiking Neural Network (SNN) enables efficient learning and recognition tasks. To achieve a large scale network akin to biology, a power and area efficient electronic neuron is essential. Earlier, we had demonstrated an LIF neuron by a novel 4-terminal impact ionization based n+/p/n+ with an extended gate (gated-INPN) device by physics simulation. Excellent improvement in area and power compared to conventional analog circuit implementations was observed. In this paper, we propose and experimentally demonstrate a compact conventional 3-terminal partially depleted (PD) SOI-MOSFET (100 nm gate length) to replace the 4-terminal gated-INPN device. Impact ionization (II) induced floating body effect in SOI-MOSFET is used to capture LIF neuron behavior to demonstrate spiking frequency dependence on input. MHz operation enables attractive hardware acceleration compared to biology. Overall, conventional PD-SOI-CMOS technology enables very-large-scale-integration (VLSI) which is essential for biology scale (similar to 10(11) neuron based) large neural networks.
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机译：受神经生物学启发的尖峰神经网络（SNN）可实现高效的学习和识别任务。为了实现类似于生物学的大规模网络，功率和面积高效的电子神经元至关重要。之前，我们通过物理仿真通过新型的基于4端碰撞电离的n + / p / n +以及扩展门（门控INPN）设备展示了LIF神经元。与传统的模拟电路实现相比，在面积和功耗上都有了极大的改善。在本文中，我们提出并通过实验演示了一种紧凑的常规3端部分耗尽（PD）SOI-MOSFET（栅极长度为100 nm），以代替4端门控INPN器件。 SOI-MOSFET中的碰撞电离（II）诱导的浮体效应用于捕获LIF神经元行为，以证明尖峰频率对输入的依赖性。与生物学相比，MHz运行可实现有吸引力的硬件加速。总体而言，常规的PD-SOI-CMOS技术可实现超大规模集成（VLSI），这对于生物学规模（类似于基于10（11）神经元的大型神经网络）至关重要。
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