3D integration enables stacking DRAM layers on processor cores within the same chip. On-chip memory has the potential to dramatically improve performance due to lower memory access latency and higher bandwidth. Higher core performance increases power density, requiring a thorough evaluation of the tradeoff between performance and temperature. This paper presents a comprehensive framework for exploring the power, performance, and temperature characteristics of 3D systems with on-chip DRAM. Utilizing this framework, we quantify the performance improvement as well as the power and thermal profiles of parallel workloads running on a 16-core 3D system with on-chip DRAM. The 3D system improves application performance by 72.6% on average in comparison to an equivalent 2D chip with off-chip memory. Power consumption per core increases by up to 32.7%. The increase in peak chip temperature, however, is limited to 1.5 o C as the lower power DRAM layers share the heat of the hotter cores. Experimental results show that while DRAM stacking is a promising technique for high-end systems, efficient thermal management strategies are needed in embedded systems with cost or space restrictions to compensate for the lack of efficient cooling.

Abstract-We present five methods to the problem of network anomaly detection. These methods cover most of the common techniques in the anomaly detection field, including Statistical Hypothesis Tests (SHT), Support Vector Machines (SVM) and clustering analysis. We evaluate all methods in a simulated network that consists of nominal data, three flowlevel anomalies and one packet-level attack. Through analyzing the results, we point out the advantages and disadvantages of each method and conclude that combining the results of the individual methods can yield improved anomaly detection results.

We present five methods to the problem of network anomaly detection. These methods cover most of the common techniques in the anomaly detection field, including Statistical Hypothesis Tests (SHT), Support Vector Machines (SVM) and clustering analysis. We evaluate all methods in a simulated network that consists of nominal data, three flowlevel anomalies and one packet-level attack. Through analyzing the results, we point out the advantages and disadvantages of each method and conclude that combining the results of the individual methods can yield improved anomaly detection results.