HYPERLOCK: In-Memory Hyperdimensional Encryption in Memristor Crossbar Array
This work proposes a novel encryption method for hardware security, though it is incremental as it builds on existing memristor and hyperdimensional computing techniques.
The paper tackles the challenge of implementing cryptography using memristor crossbar arrays by leveraging their stochastic properties and binary hypervectors, achieving 100% decryption accuracy for text despite circuit noise.
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.