Title :
A feedforward neural network for CMOS VLSI implementation
Author :
Salam, Fathi M A ; Choi, Myung-Ryul
Author_Institution :
Dept. of Electr. Eng., Michigan State Univ., East Lansing, MI, USA
Abstract :
A feedforward neural network circuit model suitable for CMOS VLSI implementation is introduced. The model captures the principles of operation of artificial neural nets, and is suitable for analog all-MOS VLSI circuit implementations. Each unit consists of a control device and one operational amplifier which is implemented with two CMOS inverters in series. A control device is implemented with one n-MOS and one p-MOS transistors to model biasing. A single n-MOS transistor is used to connect the output of a unit of one layer to the input of the next layer. All the connection weights are set by applying analog signals to the gates of these transistors
Keywords :
CMOS integrated circuits; VLSI; analogue computer circuits; network synthesis; neural nets; semiconductor device models; CMOS VLSI implementation; CMOS inverters; analogue IC; artificial neural nets; circuit model; connection weights; feedforward neural network; model biasing; operational amplifier; Artificial neural networks; Circuits; Feedforward neural networks; Neural networks; Neurofeedback; Neurons; Semiconductor device modeling; Silicon; Very large scale integration; Voltage;
Conference_Titel :
Circuits and Systems, 1989., Proceedings of the 32nd Midwest Symposium on
Conference_Location :
Champaign, IL
DOI :
10.1109/MWSCAS.1989.101898
