An Ultra Energy Efficient Neuron enabled by Tunneling in Sub-threshold Regime on a Highly Manufacturable 32 nm SOI CMOS Technology

T. Chavan, S. Dutta, N. Mohapatra, U. Ganguly
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引用次数: 6

Abstract

Human brain is a seemingly random network of $\sim 10^{11}$ neurons connected by $\sim 10^{14}$ synapses, beating today's best supercomputers by $\sim 10^{6}\times$ in energy efficiency (fig. 1). Hardware realization of such a biological network requires compact, energy efficient electronic analogs on a sufficiently matured technology. Several CMOS based analog/digital implementations suffer from large area and power consumption [1] [2]. Non-CMOS implementation of neurons may provide area/energy efficiency, but they pose fabrication challenges [3]–[5]. Earlier, our group demonstrated an energy efficient neuron on a highly manufacturable 32 nm SOI CMOS technology [6]. Impact ionization (II) based hole storage was utilized to obtain the neuronal behavior in this compact PD-SOI neuron. However, the range of operation lies in the saturation region of the transistor. This causes large current flowing through it, which adds to the power consumption. Here, we propose tunneling based hole storage enabling equivalent functionality in the SOI neuron. Unlike II, tunneling is dominant in the sub-threshold regime. Hence, the same functionality is achievable at $10^{3}\times$ lower power at sub-threshold. Thus, tunneling based neuron meets all the requirements of low energy operation, high manufacturability, and CMOS compatibility.
在高度可制造的32nm SOI CMOS技术上,亚阈值隧道化实现了超节能神经元
人脑是一个由10^{11}$神经元组成的看似随机的网络,由10^{14}$突触连接,在能量效率上比当今最好的超级计算机高出10^{6}$(图1)。这样一个生物网络的硬件实现需要在足够成熟的技术上实现紧凑、节能的电子模拟。一些基于CMOS的模拟/数字实现受到大面积和功耗的影响[1][2]。非cmos实现的神经元可能提供面积/能量效率,但它们带来了制造挑战[3]-[5]。早些时候,我们的团队在高度可制造的32纳米SOI CMOS技术上展示了一种节能神经元[6]。基于冲击电离(II)的空穴存储被用来获得这个紧凑的PD-SOI神经元的神经元行为。然而,工作范围是在晶体管的饱和区域。这会导致大电流流过它,这增加了功率消耗。在这里,我们提出了基于隧道的孔存储,在SOI神经元中实现等效功能。与II不同,隧道作用在亚阈值状态下占主导地位。因此,在亚阈值下,以$10^{3}\乘以$低的功耗可以实现相同的功能。因此,基于隧道的神经元满足低能量运行、高可制造性和CMOS兼容性的所有要求。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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