  Memristive neural networks (MNNs), which use memristors as neurons or
synapses, have become a hot research topic recently. However, most memristors
are not compatible with mainstream integrated circuit technology and their
stabilities in large-scale are not very well so far. In this paper, a hardware
friendly MNN circuit is introduced, in which the memristive characteristics are
implemented by digital integrated circuit. Through this method, spike timing
dependent plasticity (STDP) and unsupervised learning are realized. A weight
sharing mechanism is proposed to bridge the gap of network scale and hardware
resource. Experiment results show the hardware resource is significantly saved
with it, maintaining good recognition accuracy and high speed. Moreover, the
tendency of resource increase is slower than the expansion of network scale,
which infers our method's potential on large scale neuromorphic network's
realization.
