@inproceedings{6a131ee371f044c38217a406e0da9a6a,
title = "Leveraging dual-mode magnetic crossbar for ultra-low energy in-memory data encryption",
abstract = "The logic-in-memory architecture is highly promising for high-throughput data-driven applications. This paper presents a novel dual-mode magnetic crossbar architecture consisting of perpendicularly cross-coupled magnetic racetrack nanowires, which could morph between non-volatile multi-bit racetrack memory mode and in-memory data encryption mode. The proposed magnetic crossbar is able to automatically perform parallel in-memory bit-wise XOR computations of the data stored in the racetrack memories with the help of magnetic coupling physics without complex peripheral circuits, which could be leveraged to design energy efficient in-memory data encryption engine. We employ Advanced Encryption Standard (AES) algorithm to elucidate the efficiency of the proposed design. The device-to-architecture level simulation results show that the proposed architecture can achieve 70% and 17.5% lower energy consumption compared to CMOSASIC and recent domain wall (DW) AES implementations, respectively. In addition, the AES encryption speed increases by 29.7% compared to the DW-AES implementation.",
keywords = "In-memory encryption, Magnetic coupling, Magnetic crossbar",
author = "Zhezhi He and Shaahin Angizi and Farhana Parveen and Deliang Fan",
year = "2017",
month = may,
day = "10",
doi = "10.1145/3060403.3060460",
language = "English (US)",
series = "Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI",
publisher = "Association for Computing Machinery",
pages = "83--88",
booktitle = "GLSVLSI 2017 - Proceedings of the Great Lakes Symposium on VLSI 2017",
note = "27th Great Lakes Symposium on VLSI, GLSVLSI 2017 ; Conference date: 10-05-2017 Through 12-05-2017",
}