An analog implementation of radial basis neural networks (RBNN) using BiCMOS technology

Abstract

This paper describes a analog implementation of radial basis neural networks (RBNN) in BiCMOS technology. The RBNN uses a gaussian function obtained through the characteristic of the bipolar differential pair. The gaussian parameters (gain, center and width) is changed with programmable current source. Results obtained with PSPICE software is showed.

A Low-Power Analog Integrated Euclidean Distance Radial Basis Function Classifier

“…A subsection consolidates the distinctive features of system-level implementations integrated with Gaussian function circuitry. The proposed ML systems encompass various neural networks such as radial basis function neural networks (RBF NNs) [39][40][41][42][43][44][45][46][47][48][49], offering a comprehensive design framework. The related works [41,44,45,48,50] have fabricated and tested the classifier.…”

“…Based on the above analysis and bibliography, most classifiers presented have been general-purpose. More specifically, they presented a generalized topology and tested it on toy datasets [39][40][41][42][43][44][45][46][47][48][49]. On the other hand, there are application-specific implementations that combine data from real-world datasets related to biomedical engineering, computer vision, image classification, navigation, fuzzy control, sensor fusion, etc.…”

Analog Gaussian Function Circuit: Architectures, Operating Principles and Applications

“…In most cases, digital or other analog circuits are attached around a partially tunable Gaussian or Gaussian-like function circuit (Figure 28) to improve the tunability of the Gaussian function curve's three characteristics (height, mean value and variance). The extra components include multiplexers (MUX) [11,81] and/or switches [15,[82][83][84][85] and other digital circuitry (mixed-mode architectures [86]) [82][83][84], series of resistors [19,87,88], DACs [22,28,87] or Analog to Digital converters (ADC) [22], multipliers [28,31,33,87] or tunable current mirrors [89,90], OTAs [91][92][93][94] or other amplifiers [12,87], common mode feedback circuits (CMFB) [22,95], squarers [33], exponentiators [87], current-controlled current-conveyor second generation (CCII) circuits [90], minimum value circuits [96] and additional current correlators [97]. Four representative examples are provided in Figures 29-32.…”

“…Moreover, DACs, multipliers, squarers or tunable current mirrors usually directly affect the height of the Gaussian function [22,28,31,33,87,89,90]. There are implementations that use OTAs as current to voltage converters [91] or deploy three OTAs along with multiple resistors as basic building blocks to design tunable Gaussian function circuits [92][93][94]. Similarly, CCIIs, exponentiators, additional current correlators or minimum value circuits are used as basic building blocks in [87,90,96,97].…”

“…In Table 7, we summarize some common characteristics of the system level implementations, presented along with the Gaussian function circuit. The proposed ML systems are RBF NNs [11,12,14,16,24,28,61,81,94,101], a general design flow is shown in Figure 37 or other NNs, like a Multi-layer Perceptron (MLP)/RBF network (RBFN) [15] or a Gaussian RBF NN (GRBF NN) [87], Support Vector Machine (SVM) [41,62], regression (SVR) [4] or domain description (SVDD) [42] algorithms, pattern-matching classifiers [66,68], vector quantizers [64,99], a Deep ML (DML) engine [45], a similarity evaluation circuit [67] and an SOM [39]. A typical example of an Analog-hardware implementation of the SVM algorithm is shown in Figure 38.…”