Artificial Neural Network Based on Memristive Circuit for High-Speed Equalization

The limitations of traditional von Neumann architectures and digital computing are the bottlenecks for high-speed signal processing capabilities, not to mention the explosion of information growth. To tackle this challenge, this paper proposes an artificial neural network (ANN) equalizer based on the memristor for high-speed channel transmission at 112Gbps with 4-level pulse amplitude modulation (PAM4). To implement the PAM4 signal decision circuit based on the softmax algorithm, a comparator is used to make binary decisions for each output, and the only high-level output is further selected for the decision-making. The simulations on the PSPICE platform reveal that the number of input taps and the location of the main tap have the greatest impact on bit error rate (BER) performance. With optimal parameters, the circuit can achieve an impressive BER performance as low as 3.45E-6. To the best of our knowledge, this is the first implementation of channel equalization using memristive circuits, providing a valuable reference for analog circuit implementations of neural network equalizers.