Network processors are used in the core node of network to flexibly process packet streams. With the increase of performance, the power of network processor increases fast, and power and cooling become a bottleneck. A...Network processors are used in the core node of network to flexibly process packet streams. With the increase of performance, the power of network processor increases fast, and power and cooling become a bottleneck. Architecture-level power conscious design must go beyond low-level circuit design. Architectural power and performance tradeoff should be considered at the same time. Simulation is an efficient method to design modem network processor before making chip. In order to achieve the tradeoff between performance and power, the processor simulator is used to design the architecture of network processor. Using Netbeneh, Commubench benchmark and processor simulator-SimpleScalar, the performance and power of network processor are quantitatively evaluated. New performance tradeoff evaluation metric is proposed to analyze the architecture of network processor. Based on the high performance lnteI IXP 2800 Network processor eonfignration, optimized instruction fetch width and speed ,instruction issue width, instruction window size are analyzed and selected. Simulation resuits show that the tradeoff design method makes the usage of network processor more effectively. The optimal key parameters of network processor are important in architecture-level design. It is meaningful for the next generation network processor design.展开更多
A new motion retargeting algorithm is presented, which adapts me motion capture data to a new character. To make the resulting motion realistic, the physically-based optimization method is adopted. However, the optimi...A new motion retargeting algorithm is presented, which adapts me motion capture data to a new character. To make the resulting motion realistic, the physically-based optimization method is adopted. However, the optimization process is difficult to converge to the optimal value because of high complexity of the physical human model. In order to address this problem, an appropriate simplified model automatically determined by a motion analysis technique is utilized, and then motion retargeting with this simplified model as an intermediate agent is implemented. The entire motion retargeting algorithm involves three steps of nonlinearly constrained optimization： forward retargeting, motion scaling and inverse retargeting. Experimental results show the validity of this algorithm.展开更多
基金Sponsored by the National Defence Research Foundation of China(Grant No.413460303).
文摘Network processors are used in the core node of network to flexibly process packet streams. With the increase of performance, the power of network processor increases fast, and power and cooling become a bottleneck. Architecture-level power conscious design must go beyond low-level circuit design. Architectural power and performance tradeoff should be considered at the same time. Simulation is an efficient method to design modem network processor before making chip. In order to achieve the tradeoff between performance and power, the processor simulator is used to design the architecture of network processor. Using Netbeneh, Commubench benchmark and processor simulator-SimpleScalar, the performance and power of network processor are quantitatively evaluated. New performance tradeoff evaluation metric is proposed to analyze the architecture of network processor. Based on the high performance lnteI IXP 2800 Network processor eonfignration, optimized instruction fetch width and speed ,instruction issue width, instruction window size are analyzed and selected. Simulation resuits show that the tradeoff design method makes the usage of network processor more effectively. The optimal key parameters of network processor are important in architecture-level design. It is meaningful for the next generation network processor design.
文摘A new motion retargeting algorithm is presented, which adapts me motion capture data to a new character. To make the resulting motion realistic, the physically-based optimization method is adopted. However, the optimization process is difficult to converge to the optimal value because of high complexity of the physical human model. In order to address this problem, an appropriate simplified model automatically determined by a motion analysis technique is utilized, and then motion retargeting with this simplified model as an intermediate agent is implemented. The entire motion retargeting algorithm involves three steps of nonlinearly constrained optimization： forward retargeting, motion scaling and inverse retargeting. Experimental results show the validity of this algorithm.
