  We present a novel cryptography architecture based on memristor crossbar
array, binary hypervectors, and neural network. Utilizing the stochastic and
unclonable nature of memristor crossbar and error tolerance of binary
hypervectors and neural network, implementation of the algorithm on memristor
crossbar simulation is made possible. We demonstrate that with an increasing
dimension of the binary hypervectors, the non-idealities in the memristor
circuit can be effectively controlled. At the fine level of controlled crossbar
non-ideality, noise from memristor circuit can be used to encrypt data while
being sufficiently interpretable by neural network for decryption. We applied
our algorithm on image cryptography for proof of concept, and to text
en/decryption with 100% decryption accuracy despite crossbar noises. Our work
shows the potential and feasibility of using memristor crossbars as an
unclonable stochastic encoder unit of cryptography on top of their existing
functionality as a vector-matrix multiplication acceleration device.
