【Title】 This study explores the optimal spatial allocation of initial attack resources for firefighting in the Republic of Korea. To improve the effectiveness of Korean initial attack resources with a range of polic...【Title】 This study explores the optimal spatial allocation of initial attack resources for firefighting in the Republic of Korea. To improve the effectiveness of Korean initial attack resources with a range of policy goals, we create a scenario optimization model that minimizes the expected number of fires not receiving a predefined response. In this study, the predefined response indicates the number of firefighting resources that must arrive at a fire before the fire escapes and becomes a large fire. We use spatially explicit GIS-based information on the ecology, fire behavior, and economic characterizations important in Korea. The data include historical fire events in the Republic of Korea from 1991 to 2007, suppression costs, and spatial information on forest fire extent. Interviews with forest managers inform the range of we address in the decision model. Based on the geographic data, we conduct a sensitivity analysis by varying the parameters systematically. Information on the relative importance of the components of the settings helps us to identify “rules of thumb” for initial attack resource allocations in particular ecological and policy settings.展开更多
General-purpose edge neural networks need a lightweight architecture that effectively balances storage and computing resources.However,SRAM-based computing-in-memory(CIM)architectures face challenges in delivering ade...General-purpose edge neural networks need a lightweight architecture that effectively balances storage and computing resources.However,SRAM-based computing-in-memory(CIM)architectures face challenges in delivering adequate on-chip storage while fulfilling computing requirements.To overcome this,we introduce a new MRAM-based near-memory computing(NMC)architecture.It retains the costeffective data access benefits of CIM while separating storage and computing at the macro-level,improving deployment adaptability.We refine the NMC macro by incorporating small temporary storage and adopting a layer-fusion approach to enhance data-transfer efficiency.By integrating a high-capacity MRAM into the macro,we attain a storage density of 0.532 um2/bit.Moreover,we enhance the adder tree with a shift module,supporting multiply-and-accumulate(MAC)operations at five distinct depths(8,9,16,32,and 64),which raises resource utilization efficiency to 88.3%.Our architecture achieves an on-chip storage density of 1.49 Mb/mm2 and an energy efficiency of 6.164 TOPS/W.展开更多
文摘【Title】 This study explores the optimal spatial allocation of initial attack resources for firefighting in the Republic of Korea. To improve the effectiveness of Korean initial attack resources with a range of policy goals, we create a scenario optimization model that minimizes the expected number of fires not receiving a predefined response. In this study, the predefined response indicates the number of firefighting resources that must arrive at a fire before the fire escapes and becomes a large fire. We use spatially explicit GIS-based information on the ecology, fire behavior, and economic characterizations important in Korea. The data include historical fire events in the Republic of Korea from 1991 to 2007, suppression costs, and spatial information on forest fire extent. Interviews with forest managers inform the range of we address in the decision model. Based on the geographic data, we conduct a sensitivity analysis by varying the parameters systematically. Information on the relative importance of the components of the settings helps us to identify “rules of thumb” for initial attack resource allocations in particular ecological and policy settings.
基金supported in part by the Open Project Program of Anhui Province Key Laboratory of Spintronic Chip Research and Manufacturing under Grant WNKFKT-25-01in part by the National Science Foundation of China under Grant 62104025in part by the State Key Laboratory of Computer Architecture(ICT,CAS)under Grant CLQ202305.
文摘General-purpose edge neural networks need a lightweight architecture that effectively balances storage and computing resources.However,SRAM-based computing-in-memory(CIM)architectures face challenges in delivering adequate on-chip storage while fulfilling computing requirements.To overcome this,we introduce a new MRAM-based near-memory computing(NMC)architecture.It retains the costeffective data access benefits of CIM while separating storage and computing at the macro-level,improving deployment adaptability.We refine the NMC macro by incorporating small temporary storage and adopting a layer-fusion approach to enhance data-transfer efficiency.By integrating a high-capacity MRAM into the macro,we attain a storage density of 0.532 um2/bit.Moreover,we enhance the adder tree with a shift module,supporting multiply-and-accumulate(MAC)operations at five distinct depths(8,9,16,32,and 64),which raises resource utilization efficiency to 88.3%.Our architecture achieves an on-chip storage density of 1.49 Mb/mm2 and an energy efficiency of 6.164 TOPS/W.
