Previously,a single data-path stack was adequate for data-path chips,and the complexity and size of the data-path was comparatively small.As current data-path chips,such as system-on-a-chip (SOC),become more complex,m...Previously,a single data-path stack was adequate for data-path chips,and the complexity and size of the data-path was comparatively small.As current data-path chips,such as system-on-a-chip (SOC),become more complex,multiple data-path stacks are required to implement the entire data-path.As more data-path stacks are integrated into SOC,data-path is becoming a critical part of the whole giga-scale integrated circuits (GSI) design.The traditional physical design methodology can not satisfy the data-path performance requirements,because it can not accommodate the data-path bit-sliced structure and the strict performance (such as timing,coupling,and crosstalk) constraints.Challenges in the data-path physical design are addressed.The fundamental problems and key technologies in data-path physical design are analysed.The corresponding researches and solutions in this research field are also discussed.展开更多
The spatial scale(?shing grid) of ?sheries research af fects the observed spatial patterns of?sheries resources such as catch-per-unit-ef fort(CPUE) and ?shing ef fort. We examined the scale impact of high value(HH) c...The spatial scale(?shing grid) of ?sheries research af fects the observed spatial patterns of?sheries resources such as catch-per-unit-ef fort(CPUE) and ?shing ef fort. We examined the scale impact of high value(HH) clusters of the annual ?shing ef fort for Dosidicus gigas of fshore Peru from 2009 to 2012.For a multi-scale analysis, the original commercial ?shery data were tessellated to twelve spatial scales from 6′ to 72′ with an interval of 6′. Under these spatial scales, D. gigas clusters were identi?ed using the Anselin Local Moran's I. Statistics including the number of points, mean CPUE, standard deviation(SD),skewness, kurtosis, area and centroid were calculated for these HH clusters. We found that the z-score of global Moran's I and the number of points for HH clusters follow a power law scaling relationship from2009 to 2012. The mean ef fort and its SD also follow a power law scaling relationship from 2009 to 2012.The skewness follows a linear scaling relationship in 2010 and 2011 but ?uctuates with spatial scale in2009 and 2012; kurtosis follows a logarithmic scale relationship in 2009, 2011 and 2012 but a linear scale relationship in 2010. Cluster area follows a power law scaling relationship in 2010 and 2012, a linear scaling relationship in 2009, and a quadratic scaling relationship in 2011. Based on the peaks of Moran's I indices and the multi-scale analysis, we conclude that the optimum scales are 12′ in 2009 ? 2011 and 6′ in 2012, while the coarsest allowable scales are 48′ in 2009, 2010 and 2012, and 60′ in 2011. Our research provides the best spatial scales for conducting spatial analysis of this pelagic species, and provides a better understanding of scaling behavior for the ?shing ef fort of D. gigas in the of fshore Peruvian waters.展开更多
文摘Previously,a single data-path stack was adequate for data-path chips,and the complexity and size of the data-path was comparatively small.As current data-path chips,such as system-on-a-chip (SOC),become more complex,multiple data-path stacks are required to implement the entire data-path.As more data-path stacks are integrated into SOC,data-path is becoming a critical part of the whole giga-scale integrated circuits (GSI) design.The traditional physical design methodology can not satisfy the data-path performance requirements,because it can not accommodate the data-path bit-sliced structure and the strict performance (such as timing,coupling,and crosstalk) constraints.Challenges in the data-path physical design are addressed.The fundamental problems and key technologies in data-path physical design are analysed.The corresponding researches and solutions in this research field are also discussed.
基金Supported by the National Natural Science Foundation of China(No.41406146)the Laboratory for Marine Fisheries Science and Food Production Processes at Qingdao National Laboratory for Marine Science and Technology of China(No.2017-1A02)the Shanghai Universities First-class Disciplines Project-Fisheries(A)
文摘The spatial scale(?shing grid) of ?sheries research af fects the observed spatial patterns of?sheries resources such as catch-per-unit-ef fort(CPUE) and ?shing ef fort. We examined the scale impact of high value(HH) clusters of the annual ?shing ef fort for Dosidicus gigas of fshore Peru from 2009 to 2012.For a multi-scale analysis, the original commercial ?shery data were tessellated to twelve spatial scales from 6′ to 72′ with an interval of 6′. Under these spatial scales, D. gigas clusters were identi?ed using the Anselin Local Moran's I. Statistics including the number of points, mean CPUE, standard deviation(SD),skewness, kurtosis, area and centroid were calculated for these HH clusters. We found that the z-score of global Moran's I and the number of points for HH clusters follow a power law scaling relationship from2009 to 2012. The mean ef fort and its SD also follow a power law scaling relationship from 2009 to 2012.The skewness follows a linear scaling relationship in 2010 and 2011 but ?uctuates with spatial scale in2009 and 2012; kurtosis follows a logarithmic scale relationship in 2009, 2011 and 2012 but a linear scale relationship in 2010. Cluster area follows a power law scaling relationship in 2010 and 2012, a linear scaling relationship in 2009, and a quadratic scaling relationship in 2011. Based on the peaks of Moran's I indices and the multi-scale analysis, we conclude that the optimum scales are 12′ in 2009 ? 2011 and 6′ in 2012, while the coarsest allowable scales are 48′ in 2009, 2010 and 2012, and 60′ in 2011. Our research provides the best spatial scales for conducting spatial analysis of this pelagic species, and provides a better understanding of scaling behavior for the ?shing ef fort of D. gigas in the of fshore Peruvian waters.
