TSV-interconnected 3D chips face problems such as high cost, low yield and large power dissipation. We propose a wireless 3D on-chip-network architecture for application-specific SoC design, using inductive-coupling interconnect instead of TSV for inter-layer communication. Primary design challenge of inductive-coupling 3D SoC is allocating wireless links in the 3D on-chip network effectively. We develop a design flow fully exploiting the design space brought by wireless links while providing flexible tradeoff for user's choice. Experimental results show that our design brings great improvement over uniform design and Sunfloor algorithm on latency (5% to 20%) and power consumption (10% to 45%).

Online resource management of a software system can take advantage of a performance model to predict the effect of proposed changes. However, the prediction accuracy may degrade if the performance model does not adapt to the changes in the system. This work considers the problem of using Kalman filters to track changes in both performance model parameters and system behavior. We propose a method based on the multiple-model Kalman filter. The method runs a set of Kalman filters, each of which models different system behavior, and adaptively fuses the output of those filters for overall estimates. We conducted case studies to demonstrate how to use the method to track changes in various system behaviors: performance modeling, process modeling, and measurement noise. The experiments show that the method can detect changes in system behavior promptly and significantly improve the tracking and prediction accuracy over the single-model Kalman filter. The influence of model design parameters and mode-model mismatch is evaluated. The results support the usefulness of the multiple-model Kalman filter for tracking performance model parameters in systems with time-varying behavior.

To efficiently utilize storage resources, the in-network caching system of Information-Centric Networking has to deal with the popularity of huge content chunks which could cause large memory consumption. This paper presents a Popularity Monitoring based Gain-aware caching scheme, called PMG, which is an integrated design of cache placement and popularity monitoring. In PMG, by taking into account both the chunk popularity and the consumption saving of single cache hit, the cache placement process is transformed into a weighted popularity comparison, while the chunks with high cache gain are placed on the node closer to the content consumer. A Bloom Filter based sliding window algorithm, which is self-adaptive to the dynamic request rate, is proposed to capture the chunks with higher caching gain by Inter-Reference Gap (IRG) detection. Analysis shows that PMG can drastically reduce the memory consumption of popularity monitoring, and the simulation results confirm that our scheme can achieve popularity based cache placement and get better performance in terms of bandwidth saving and cache hit ratio when content popularity changes dynamically.

Hybrid wired/wireless on-chip network is a promising communication architecture for multi-/many-core SoC. For application-specific SoC design, it is important to design a dedicated on-chip network architecture according to the application-specific nature. In this paper, we propose a heuristic wireless link allocation algorithm for creating hybrid on-chip network architecture. The algorithm can eliminate the performance bottleneck by replacing multi-hop wired paths by high-bandwidth single-hop long-range wireless links. The simulation results show that the hybrid on-chip network designed by our algorithm improves the performance in terms of both communication delay and energy consumption significantly.

The recommend system has been widely used in many web application areas such as e-commerce services. With the development of the recommend system, the HIN modeling method replaces the traditional bipartite graph modeling method to represent the recommend system. But several studies have already showed that recommend system is vulnerable to shilling attack (injecting attack). However, the effectiveness of how traditional shilling attack has rarely been studied directly in the HIN model. Moreover, no study has focused on how to enhance shilling attacks against HIN recommend system by using the high-level semantic information. This work analyzes the relationship between the high-level semantic information and the attacking effects in HIN recommend system. This work proves that attack results are proportional to the high-level semantic information. Therefore, we propose a heuristic attack method based on high-level semantic information, named Semantic Shilling Attack (SSA) on a HIN recommend system (HERec). This method injects a specific score into each selected item related to the target in semantics. It ensures transmitting the misleading information towards target items and normal users, and attempts to interfere with the effect of the recommend system. The experiment is dependent on two real-world datasets, and proves that the attacking effect is positively correlate with the number of meta-paths. The result shows that our method is more effective when compared with existing baseline algorithms.