An Analog Voltage Similarity Circuit with a Bell-Shaped Power Consumption

Abstract

This paper presents a voltage similarity circuit (bump circuit) based on a novel voltage correlator. The proposed circuit is characterized by a power consumption which depends on the similarity between the two inputs. The sensitivity of the bump circuit and the power consumption are at the highest values when the inputs are equal. As the similarity between the input voltages decreases, the total current consumption decreases with a bell-shaped behavior. The proposed bump circuit is very simple in design, made of a new voltage correlator circuit in combination with a differential pair and mimics the behavior of the classical bump circuit. The voltage correlator was implemented using AMS−350nm CMOS technology. Simulation and measurement results suggests that a low power consumption may be achieved if the circuit is used in applications where the input signals have large dissimilarity for most of the circuit operation.

Analog Gaussian Function Circuit: Architectures, Operating Principles and Applications

“…Nonetheless, they can be added into a general group. In particular, there are architectures [13,14,20,30] based on Delbruck's Simple Bump , designs [65,98] inspired by Anderson [12] or based on other function generation circuits, like a triangular [29,99], an exponential [16,100] or a Euclidean [101]. An example for each group is shown in Figures 33-36, respectively.…”

“…The power supply ranges from 1.8 V to 5 V and the minimum operational bias current is less than 1 µA (except from [29]). There are implementations operating in either the sub-threshold or the saturation region, with two of them having transistors that operate in the triode region [13,30]. Regarding the number of transistors, most architectures are generally compact.…”

“…Since the first Gaussian function circuit (also called the Bump circuit) was introduced by Delbruck (Delbruck's Simple Bump) in 1991 [1,2] (shown in Figure 1), many research groups have focused on improving this architecture and/or incorporating it in various fields [3][4][5][6][7][8][9][10][11][12][13][14]. Some of the implementations are used for Machine Learning (ML) applications [15,16], neuromorphic systems [17,18], smart sensors [19,20] and fuzzy or neuro-fuzzy systems [21,22].…”