Abstract3-D Networks-on-Chip (NoC) emerge as a potent solution to address both the interconnection and design complexity problems facing future Multiprocessor System-on-Chips (MPSoCs). Effective run-time mapping on such 3-D NoC-based MPSoCs can be quite challenging, as the arrival order and task graphs of the target applications are typically not known a priori, which can be further complicated by stringent energy requirements for NoC systems. This paper thus presents an energy-aware run-time incremental mapping algorithm (ERIM) for 3-D NoC which can minimize the energy consumption due to the data communications among processor cores, while reducing the fragmentation effect on the incoming applications to be mapped, and simultaneously satisfying the thermal constraints imposed on each incoming application. Specifically, incoming applications are mapped to cuboid tile regions for lower energy consumption of communication and the minimal routing. Fragment tiles due to system fragmentation can be gleaned for better resource utilization. Extensive experiments have been conducted to evaluate the performance of the proposed algorithm ERIM, and the results are compared against the optimal mapping algorithm (branch-and-bound) and two heuristic algorithms (TB and TL). The experiments show that ERIM outperforms TB and TL methods with significant energy saving (more than 10%), much reduced average response time, and improved system utilization.
In response to growing security challenges facing many-core systems imposed by thermal covert channel (TCC) attacks, a number of threshold-based detection methods have been proposed. In this paper, we show that these threshold-based detection methods are inadequate to detect TCCs that harness advanced signaling and specific modulation techniques. Since the frequency representation of a TCC signal is found to have multiple side lobes, this important feature shall be explored to enhance the TCC detection capability. To this end, we present a pattern-classification-based TCC detection method using an artificial neural network that is trained with a large volume of spectrum traces of TCC signals. After proper training, this classifier is applied at runtime to infer TCCs, should they exist. The proposed detection method is able to achieve a detection accuracy of 99%, even in the presence of the stealthiest TCCs ever discovered. Because of its low runtime overhead (< 0.187%) and low energy overhead (< 0.072%), this proposed detection method can be indispensable in fighting against TCC attacks in many-core systems. With such a high accuracy in detecting TCCs, powerful countermeasures, like the ones based on dynamic voltage and frequency scaling (DVFS), can be rightfully applied to neutralize any malicious core participating in a TCC attack.
Many-core systems are susceptible to attacks launched by thermal covert channel (TCC) attacks. Detection of TCC attacks often relies on the use of threshold-based approaches or variants, and a countermeasure to thwart the channel can be applied only after an attack is deemed to be present. In this paper, we describe a direct sequence spread spectrum (DSSS) based TCC, where its thermal data are modulated by a pseudorandom bit sequence. Unfortunately, such DSSS-based TCC has an extremely low signal strength that the signal is nearly indistinguishable from the noise and thus cannot be detected by any existing threshold-based detection methods. To combat this stealthy TCC, we propose a novel detection scheme that lets the received signal pass through a differential filter where irrelevant frequency components occupied mainly by the noise gets eliminated and the filtered signal is next compared against a threshold for successful detection. Experimental results show that the DSSS-based TCC can effectively survive detection by the existing detection methods with its BER as low as 4%. In contrast, with the proposed detection and countermeasure applied, the detection accuracy jumps to 89%, and the BER of the DSSS-based TCC soars to 50%, which indicates that the TCC is practically shut down.
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