Invited Paper: Rowhammer Mitigation by Approximate Computing: A Compressed Sensing Case Study
Year: 2025
Type: conference-paper
Abstract: While Approximate Computing (AC) trades the precision for energy efficiency with tolerable errors, its security of approximate data in digital storage is not well explored for edge devices. As one of the most effective hardware security attack methods, Rowhammer attack has shown significant threats to the digital data on dynamic random access memory (DRAM) with the vulnerability of high-frequency memory row access. This work performs the first preliminary evaluation of Rowhammer attack on real-world compressed sensing applications with approximate data. By investigating Rowhammer attack on the approximate data from compact LiDar sensor, the security impact of various precisions is presented through the fidelity of reconstructed depth image. The experiments reveal considerable mitigation of Rowhammer attack by adopting AC based sensor signal processing, where up to 2× higher PSNR of output depth image is achieved comparing to those with accurate data and computations.
Authors: Yuhang Hao, Yun Wu, Minmin Jiang, Máire O’Neill, Chongyan Gu
Institution: Queen's University Belfast
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: China Scholarship Council
OpenAlex ID: W4416429613