Abstract
The purpose of this work was to demonstrate the feasibility of building recurrent artificial neural networks with hybrid complementary metal oxide semiconductor (CMOS)/memristor circuits. To do so, we modeled a Hopfield network implementing an analog-to-digital converter (ADC) with up to 8 bits of precision. Major shortcomings affecting the ADC's precision, such as the non-ideal behavior of CMOS circuitry and the specific limitations of memristors, were investigated and an effective solution was proposed, capitalizing on the in-field programmability of memristors. The theoretical work was validated experimentally by demonstrating the successful operation of a 4-bit ADC circuit implemented with discrete Pt/TiO2−x/Pt memristors and CMOS integrated circuit components.
Highlights
Recurrent artificial neural networks are an important computational paradigm capable of solving a number of optimization problems (Hopfield, 1984; Tank and Hopfield, 1986)
This paper explores the implementation of synapses with an emerging, very promising type of memory devices, namely metal-oxide resistive switching devices (“memristor”) (Wu et al, 2012; Zhang et al, 2012)
The physical implementation of this Hopfield network analog-to-digital converter (ADC) posed a number of additional challenges
Summary
Recurrent artificial neural networks are an important computational paradigm capable of solving a number of optimization problems (Hopfield, 1984; Tank and Hopfield, 1986). One classic example of such networks is a Hopfield analog-to-digital converter (Tank and Hopfield, 1986; Lee and Sheu, 1989; Smith and Portmann, 1989). Such a circuit may be of little practical use, and inferior, for example, to similar-style feed forward-type ADC implementations (Chigusa and Tanaka, 1990), it belongs to a broader constrained optimization class of networks which minimize certain preprogrammed energy functions and have several applications in control and signal processing (Tank and Hopfield, 1986). Neuron output needs to be inverted to keep the feedback weights positive and to allow physical implementation with passive devices, such as resistors
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