CONCEAL: Covert NoC Exploitation in In-Memory Computing-based DNN Accelerators

Network-on-Chip (NoC) facilitates seamless intercommunication in in-memory computing (IMC)-based deep neural network (DNN) accelerators. However, these NoCs are vulnerable to flooding attacks, which generate useless packets during communication to result in significant bandwidth reduction. In this paper, for the first time, we propose CONCEAL, a novel flooding attack pertaining to NoCs in IMC-based DNN accelerators. By devising adversarially crafted stealth packets and injecting them through NoC during inter-tile communication in the accelerator, CONCEAL introduces substantial deterioration in the performance of the NoC, along with reduced computational efficiency encompassing latency and dynamic energy consumption in the accelerator. Our novel attack framework’s effectiveness is derived from the fact that it only slightly reduces the application-level classification accuracy of the accelerator. The negligible degradation in accuracy makes CONCEAL difficult to detect and renders the attack to remain stealthy. Our experimental evaluations demonstrate that the proposed CONCEAL framework can furnish up to 425.83% and 257.61% performance degradation in terms of energy and latency respectively (including both communication and computation), while only exhibiting ≤ 5% (in some cases < 1%) reduction in the classification accuracy of the IMC-based DNN accelerator.