Noise cancellation of memristive neural networks

• Scalable massively parallel architecture improves the hardware functionality.
• The state of A-GST memristive devices will decay over long time.
• The computation results can be read out without altering the states of synapses.
• A-GST memristive devices make the memristive synapses robustness.

This paper investigates noise cancellation problem of memristive neural networks. Based on the reproducible gradual resistance tuning in bipolar mode, a first-order voltage-controlled memristive model is employed with asymmetric voltage thresholds. Since memristive devices are especially tiny to be densely packed in crossbar-like structures and possess long time memory needed by neuromorphic synapses, this paper shows how to approximate the behavior of synapses in neural networks using this memristive device. Also certain templates of memristive neural networks are established to implement the noise cancellation.