This brief presents an ultra-low-power generic fully tunable analog 1-D Gaussian kernel (GK) circuit, which is employed as an activation neuron for the radial basis function artificial neural network. In the proposed GK circuit, the maximum likelihood, center, and width of the Gaussian profile can be independently controlled. Besides, we have developed a mathematical model for the proposed GK circuit and further verified them experimentally. Thereby, the presented modeling can be employed to facilitate the off-chip learning of the neural network hardware. Fabricated in 180 nm CMOS process, the prototype demonstrates the full tunability of the proposed GK circuit and good agreement between the experimental measurements and mathematical model. The relative error of the measured width of the GK’s profile is less than 7 % compared to the value predicted by the presented mathematical model. The total power consumption is 13.5 nW with a supply voltage of 0.9 V, and the core circuit occupies 0.013 mm2.

中文翻译：

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用于人工神经网络的通用纳瓦功率完全可调一维高斯核电路

本简介介绍了一种超低功耗通用完全可调模拟一维高斯核 (GK) 电路，该电路用作径向基函数人工神经网络的激活神经元。在建议的 GK 电路中，可以独立控制高斯分布的最大似然、中心和宽度。此外，我们为所提出的 GK 电路开发了一个数学模型，并通过实验进一步验证了它们。因此，可以采用所提出的建模来促进神经网络硬件的片外学习。该原型采用 180 nm CMOS 工艺制造，展示了所提出的 GK 电路的完全可调性以及实验测量与数学模型之间的良好一致性。与所提出的数学模型预测的值相比，GK 轮廓的测量宽度的相对误差小于 7%。电源电压为0.9 V时，总功耗为13.5 nW，核心电路占地0.013 mm2。