Cellular Nonlinear Networks with Memristor Synapses

Cellular Nonlinear/Nanoscale Networks (CNNs) that can provide parallel processing in massive scale are known to be suitable for neuromorphic applications such as vision systems. In CNNs, synaptic weights can be calculated by digital or analog multiplications. Though conventional CMOS digital circuits can be used in calculating these multiplications for CNN applications, they occupy very large area and consume a large amount of power, especially when multiplications should be calculated in parallel in massive scale. On the other hand, analog circuits seem to be very attractive for calculating multiplications for CNN applications. One possible approach is to multiply the input current by the programmable resistance of a memristor before applying the resulting voltage to a differential pair for the final voltage-to-current conversion. In this chapter we introduce some analog circuits for CNN applications that use the resistance of a memristor in calculating multiplications. In addition we discuss memristor models and some practical problems in CNN circuits that should be resolved using analog memristor-based implementations.

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