The thermal covert channels (TCC's) in many-core systems can cause detrimental data breaches. In this paper, we present a three-step scheme to detect and fight against such TCC attacks. Specifically, in the detection step, each core calculates the spectrum of its own CPU workload traces that are collected over a few fixed time intervals, and then it applies a frequency scanning method to detect if there exists any TCC attack. In the next positioning step, the logical cores running the transmitter threads are located. In the last step, the physical CPU cores suspiciously engaging in a TCC attack have to undertake Dynamic Voltage Frequency Scaling (DVFS) such that any possible TCC trace will be essentially wiped out. Our experiments have confirmed that on average 97% of the TCC attacks can be detected, and with the proposed defense, the packet error rate (PER) of a TCC attack can soar to more than 70%, literally shutting down the attack in practical terms. The performance penalty caused by the inclusion of the proposed DVFS countermeasures is found to be only 3% for an 8×8 many-core system.

No abstract

No abstract

Accurate and efficient data classification techniques are of vital importance to many problems, and are rapidly developing in recent decades. K-Nearest Neighbor algorithm (KNN), as one of the most important algorithms, is widely used in text categorization, predictive analysis, data mining and image recognition, etc. To accelerate the algorithm and to optimize the parallel implementation solution are two key issues of KNN. In this paper, we propose a new solution to speed up KNN algorithm on FPGA based heterogeneous computing system using OpenCL. Based on FPGA's parallel pipeline structure, a specific bubble sort algorithm is designed to optimize KNN algorithm. The results have been shown that the efficiency of the solution in our paper is much higher than conventional GPU based KNN algorithm implementation.

No abstract