Cai, Jack, Azghadi, Mostafa Rahimi, Genov, Roman, and Amirsoleimani, Amirali (2025) HyperXArray: Low-power and Compact Memristive Architecture for In-Memory Encryption on Edge. IEEE Transactions on Emerging Topics in Computing, 13 (4). pp. 1410-1423.

PDF (Published Version) - Published Version Restricted to Repository staff only

Abstract

Encryption on large-scale memristor crossbars proves to be challenging due to the spatial and temporal fluctuations of the signals coming from numerous non-idealities. To address this, we utilize Hyperlock, a memristive vector-matrix multiplication accelerator employing hyperdimensional computing for encryption. We demonstrate that stochasticity generated on TiOx memristor crossbars with passive 0T1R arrangement can be decryptable under the appropriate training of a neural network. We present HyperXArray, an architecture for Hyperlock's encryption scheme, that is capable of weight regeneration, and analog/digital encryption without the need for high-resolution Analog-to-Digital Converters (ADCs) and Digital-to-Analog Converters (DACs). We demonstrate 100% decryption accuracy for digital encryption and show that HyperXArray is capable of encryption during analog to digital conversion that reduces the power consumption of ADC by 50×. In digital encryption, we show that HyperXArray reduces energy consumption by up to 10× and footprint by 10-100× compared to Field Programmable Gate Array (FPGA) implementations of Advanced Encryption Standard (AES), while maintaining the same level of throughput. Overall, HyperXArray demonstrates its capability to fill the niche for lightweight, noise-resilient encryption on edge with only 0.1mm² footprint and 60 pJ/bit energy efficiency.

Actions (Repository Staff Only)

Item Control Page