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AnalogtoDigital conversion is a basic signal processing task that is needed at various places in the context of modern day mixedsignal systems like instrumentation & control systems, systemonchip, etc. It is because of the fact that most realworld signals are analog in nature whereas most onchip computation is digital. The technical literature is replete with electronic implementations of analog to digital converters including, but not limited to, Flash ADC, Successive Approximation ADC, and SigmaDelta ADC. Given their promise of parallel processing and fast convergence, artificial neural networks have also been employed for analogtodigital conversion. The first such attempt employed the Hopfield Neural Network and later several variants were introduced. However, most of the existing neural circuits for analogtodigital conversion have an underlying similarity in the sense that they are derived from the Hopfield Network Architecture. A new scheme for analogtodigital conversion utilizing a neural circuit for solving systems of linear equations is presented. The circuit employs (2n) opamps and (n+3) resistances for an n bit ADC.
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