7672026
A power and temperature aware DRAM architecture
Technological advances enable modern processors to utilize increasingly larger DRAMs with rising access frequencies. This is leading to high power consumption and operating temperature in DRAM chips. As a result, temperature management has become a real and pressing issue in high performance DRAM systems. Traditional low power techniques are not suitable for high performance DRAM systems with high bandwidth. In this paper, we propose and evaluate a customized DRAM low power technique based on page hit aware write buffer (PHA-WB). Our proposed approach reduces DRAM system power consumption and temperature without any performance penalty. Our experiments show that a system with a 64-entry PHA-WB could reduce the total DRAM power consumption by up to 22.0% (9.6% on average). The peak and average temperature reductions are 6.1degC and 2.1degC, respectively.
10896727
Firefly: illuminating future network-on-chip with nanophotonics
Future many-core processors will require high-performance yet energy-efficient on-chip networks to provide a communication substrate for the increasing number of cores. Recent advances in silicon nanophotonics create new opportunities for on-chip networks. To efficiently exploit the benefits of nanophotonics, we propose Firefly - a hybrid, hierarchical network architecture. Firefly consists of clusters of nodes that are connected using conventional, electrical signaling while the inter-cluster communication is done using nanophotonics - exploiting the benefits of electrical signaling for short, local communication while nanophotonics is used only for global communication to realize an efficient on-chip network. Crossbar architecture is used for inter-cluster communication. However, to avoid global arbitration, the crossbar is partitioned into multiple, logical crossbars and their arbitration is localized. Our evaluations show that Firefly improves the performance by up to 57% compared to an all-electrical concentrated mesh (CMESH) topology on adversarial traffic patterns and up to 54% compared to an all-optical crossbar (OP XBAR) on traffic patterns with locality. If the energy-delay-product is compared, Firefly improves the efficiency of the on-chip network by up to 51% and 38% compared to CMESH and OP XBAR, respectively.
11247110
Sustaining Incentive in Grid Resource Allocation: A Reinforcement Learning Approach
Encouraging resource sharing and cooperation among different parties is one of the central goals of grid computing. In real environments, however, selfish or malicious nodes can seriously degrade the sharing and cooperation performance of a grid. To solve this problem, we propose QIA, a novel Q-learning based resource Allocation mechanism that sustains Incentive for every participating node. Exploiting an economic model, QIA recognizes the importance of trust factor when allocating resources. Each provider considers a combined metric, which is composed of the bid price and the trust value, of a requester when allocating its resources. The incomplete information is a key issue for a provider in determining the relative weight of bid price and trust value. We propose a reinforcement Q- learning technique to resolve the issue, which is able to adapt the dynamics of grid environments. We implemented QIA in a real grid test-bed, CROWN grid. Comprehensive experiments have been conducted, which demonstrate the efficacy of QIA.
17747144
Distributed access control in CROWN groups
Security in collaborative groups is an active research topic and has been recognized by many organizations in the past few years. In this paper, we propose a fine-grained and attribute-based access control framework for our key project, CROWN grid. To avoid single point of failure and enhance scalability of the system, we employ a distributed delegation authorization mechanism. We successfully implement our proposed access control in CROWN grid, and evaluate this approach by comprehensive experiments.
7761771
Access control in peer-to-peer collaborative systems
As an emerging model of communication and computation, peer-to-peer networking represents a fully distributed, cooperative network design, and has recently gained significant acceptance. Peer groups share the properties of peer-to-peer overlay network, including full decentralization, symmetric abilities, and dynamism, which make security problems more complicated. In this paper, we propose a fine-grained and attribute-based access control framework for peer-to-peer systems. This design employs a novel policy model, which extends role-based trust management language RT to satisfy security requirements of peer groups. Intend for a pure decentralized model without centralized server, our framework presents distributed delegation authorization mechanism, which could avoid single point of failure. We also introduce our implementation experience.
6412815
Automated task distribution in multicore network processors using statistical analysis
Chip multiprocessor designs are the most common types of architectures seen in Network Processors. As the Network Processors are used to implement increasingly complicated applications, task distribution among the cores is becoming an important problem. In this paper, we propose a new task allocation scheme for such architectures. This scheme relies on the inherent modular nature of the networking applications and intelligently distributes modules among different execution cores. Additionally, we selectively replicate modules to parallelize execution of tasks having longer processing time. We have developed a technique that uses the probability distribution of the execution times of different modules in the networking applications. The proposed schemes result in resource utilization of up to 95%, 89%, and 84% on average for the processors with 2, 4, and 8 cores, respectively. The schemes are highly scalable and can improve the throughput by 6.72 times for 8 core processors, aggregated over four representative applications. The combination of selective replication of modules and variation-aware task allocation result in up to 12.5% (9.9% on average) performance improvement as compared to a scheme based on just mean processing time.
16395245
Hierarchical face modeling and fast 3D facial expression synthesis
This paper presents a new hierarchical facial model that conforms to the human anatomy for realistic and fast 3D facial expression synthesis. The facial model has a skin/muscle/skull structure. The deformable skin model uses a kind of nonlinear spring to directly simulate the nonlinear visco-elastic behavior of soft tissue, and a new kind of edge repulsion spring is developed to prevent model collapse. The incorporation of the skull extends the scope of facial motion and facilitates facial muscle construction. The construction of facial muscles is achieved by using an efficient muscle mapping approach that ensures different muscles to be located at the anatomically correct positions. For computational efficiency, we devise an adaptive simulation algorithm which uses either a semi-implicit integration scheme or a quasi-static solver to compute the relaxation by traversing the designed data structures in a breadth-first order. The algorithm runs in real-time and has successfully synthesized realistic facial expressions.
29024953
Mixed diamond, hexagon, and cross search fast motion estimation algorithm for H.264
The block motion estimation process is a very time consuming part in the H.264 video coding standard and thus hinders its real-time applications. Therefore, a new fast block motion estimation algorithm is proposed in this paper. In this algorithm, it first determines the current block as a quasi-stationary, slow motion, or fast motion block. Then, according to the different motion types, different search patterns and strategies are intelligently employed. Experimental results demonstrate that the proposed algorithm has much less execution time than the existing FS, UMHexagonS, and EPZS algorithms, with about the same accuracy.
15125228
Web Mining for Lexical Context-Specific Paraphrasing
In most applications of paraphrasing, contextual information should be considered since a word may have different paraphrases in different contexts. This paper presents a method that automatically acquires lexical context-specific paraphrases from the web. The method includes two main stages, candidate paraphrase extraction and paraphrase validation. Evaluations were conducted on a news title corpus whereby the context-specific paraphrasing method was compared with the Chinese synonymous thesaurus. Results show that the precision of our method is above 60% and the recall is above 55%, which outperforms the thesaurus significantly.
12319923
Decoding Digital Information from the Cascaded Heterogeneous Chaotic Systems
In this paper, we propose a new approach to breaking down chaotic communication scheme by attacking its encryption keys. A remarkable advancement is that it can decode the hidden message exactly. This makes it become possible to break down some cascaded chaotic communication systems. We also decode digital information from the cascaded heterogeneous chaotic communication system and give the simulation results.
6059000
Preserving User Location Privacy in Mobile Data Management Infrastructures
Location-based services, such as finding the nearest gas station, require users to supply their location information. However, a user's location can be tracked without her consent or knowledge. Lowering the spatial and temporal resolution of location data sent to the server has been proposed as a solution. Although this technique is effective in protecting privacy, it may be overkill and the quality of desired services can be severely affected. In this paper, we suggest a framework where uncertainty can be controlled to provide high quality and privacy-preserving services, and investigate how such a framework can be realized in the GPS and cellular network systems. Based on this framework, we suggest a data model to augment uncertainty to location data, and propose imprecise queries that hide the location of the query issuer and yields probabilistic results. We investigate the evaluation and quality aspects for a range query. We also provide novel methods to protect our solutions against trajectory-tracing. Experiments are conducted to examine the effectiveness of our approaches.