High-Accuracy Gaussian Function Generator for Neural Networks

被引:0
|
作者
Popa, Cosmin Radu [1 ]
机构
[1] Univ Politehn Bucuresti, Fac Elect Telecommun & Informat Technol, Bucharest 061071, Romania
关键词
Gaussian function; VLSI neural networks; analog signal processing; approximation function; current-mode operation; CIRCUIT;
D O I
10.3390/electronics12010024
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
A new improved accuracy CMOS Gaussian function generator will be presented. The original sixth-order approximation function that represents the basis for designing the proposed Gaussian circuit allows a large increase in the circuit accuracy and also of the input variable maximal range. The original proposed computational structure has a large dynamic output range of 27 dB, for a variation smaller than 1 dB as compared with the ideal Gaussian function. The circuit is simulated for 0.18 mu m CMOS technology and has a low supply voltage (V-DD = 0.7 V). Its power consumption is smaller than 0.22 mu W, for V-DD = 0.7 V, while the chip area is about 7 mu m(2). The new proposed architecture is re-configurable, the convenient modification of the coefficients allowing to obtain many mathematical functions using the same computational structure.
引用
收藏
页数:9
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