Simple Hardware Implementation Of Neural Networks For Instruction In Analog Electronics

In light of the growing predominance of microprocessors and embedded electronic systems, instruction in basic analog and digital electronic circuits has come to appear less interesting and important to contemporary students of electrical engineering. Despite the continuing importance of foundation circuit concepts, curricula across the country are reducing their emphasis in required courses or shifting them into optional courses. In hopes of mitigating this trend, we discuss a circuit system which applies traditional analog and digital MOSFET sub-circuits into a meaningful contemporary system, the neural network. Neural networks offer a unique approach for processing complex data streams without the need for digital processors. Constructed in a fashion which mimics biological nervous systems, these networks are finding applications in signal processing, control and object recognition. In many cases, a properly prepared neural network can function faster than a comparable microprocessor based system, with lower power consumption and lower level of complexity. Despite their potential and relative conceptual simplicity, it has been difficult to present electronic neural networks in a form convenient for the university classroom or electronics laboratory setting. In this paper we describe an approach for implementing a neural network though which many major analog and digital MOSFET circuit concepts can be illustrated and demonstrated. This approach is amenable to realization in discrete electronic modules through which associated laboratory exercises and design projects may be created. Furthermore, the same concepts can be extended into Very Large Scale Integration (VLSI), where the limitations of component count and performance can be overcome and addressed to a far greater degree.