Abstract
This review paper explores existing architectures, operating principles, performance metrics and applications of analog Gaussian function circuits. Architectures based on the translinear principle, the bulk-controlled approach, the floating gate approach, the use of multiple differential pairs, compositions of different fundamental blocks and others are considered. Applications involving analog implementations of Machine Learning algorithms, neuromorphic circuits, smart sensor systems and fuzzy/neuro-fuzzy systems are discussed, focusing on the role of the Gaussian function circuit. Finally, a general discussion and concluding remarks are provided.
Highlights
Since the first Gaussian function circuit was introduced by Delbruck (Delbruck’s Simple Bump) in 1991 [1,2], 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]
Implementations that add extra components, for example Operational Transconductance Amplifiers (OTAs), Digital to Analog Converters (DACs), mixed-mode circuits, and so forth, which provide the appropriate tunability in the variance
In the case of a wearable classification application, the design of a low-power and area efficient Gaussian function circuit is necessary, because its realization consists of many cells which have to operate in parallel fashion [30]
Summary
Since the first Gaussian function circuit ( 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]. Designs using exclusively differential pairs and current mirrors; Implementations that add extra components, for example Operational Transconductance Amplifiers (OTAs), Digital to Analog Converters (DACs), mixed-mode circuits, and so forth, which provide the appropriate tunability in the variance. These categories are not mutually exclusive, since there are a few implementations that may belong in more than one. In the case of a wearable classification application, the design of a low-power and area efficient Gaussian function circuit is necessary, because its realization consists of many cells which have to operate in parallel fashion [30].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
