Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Software abstractions are crucial to effectively program heterogeneous Multi-Processor Systems on Chip (MPSoCs). Prime examples of such abstractions are Kahn Process Networks (KPNs) and execution traces. When modeling computation as a KPN, one of the key challenges is to obtain a good mapping, i.e., an assignment of logical computation and communication to physical resources.   compare two system-level frameworks for solving the mapping problem: Sesame and MAPS. These frameworks, while superficially similar, embody different approaches. Sesame, motivated by modeling and design-space exploration, uses evolutionary algorithms for mapping. MAPS, being a compiler framework, uses simple and fast heuristics instead. In this work  highlight the value of common abstractions, such as KPNs and traces, as a vehicle to enable comparisons beten large independent frameworks. These types of comparisons are fundamental for advancing research in the area. At the same time,  illustrate how the lack of formalized models at the hardware level are an obstacle to achieving fair comparisons. Additionally, using a set of applications from the embedded systems domain,  observe that genetic algorithms tend to outperform heuristics by a factor beten 1× and 5×, with notable exceptions. This performance comes at the cost of a longer computation time, beten 0 and 2 orders of magnitude in our experiments.
['Andrés Goens', 'R. A. Khasanov', 'Jeronimo Castrillon', 'Simon Polstra', 'Andy D. Pimentel']
I am writing a research paper on a idea Why Comparing System-Level MPSoC Mapping Approaches is Difficult: A Case Study
[{"from": "human", "value": "I am writing a research paper on a idea Why Comparing System-Level MPSoC Mapping Approaches is Difficult: A Case Study"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Software abstractions are crucial to effectively program heterogeneous Multi-Processor Systems on Chip (MPSoCs). Prime examples of such abstractions are Kahn Process Networks (KPNs) and execution traces. When modeling computation as a KPN, one of the key challenges is to obtain a good mapping, i.e., an assignment of logical computation and communication to physical resources.   compare two system-level frameworks for solving the mapping problem: Sesame and MAPS. These frameworks, while superficially similar, embody different approaches. Sesame, motivated by modeling and design-space exploration, uses evolutionary algorithms for mapping. MAPS, being a compiler framework, uses simple and fast heuristics instead. In this work  highlight the value of common abstractions, such as KPNs and traces, as a vehicle to enable comparisons beten large independent frameworks. These types of comparisons are fundamental for advancing research in the area. At the same time,  illustrate how the lack of formalized models at the hardware level are an obstacle to achieving fair comparisons. Additionally, using a set of applications from the embedded systems domain,  observe that genetic algorithms tend to outperform heuristics by a factor beten 1\u00d7 and 5\u00d7, with notable exceptions. This performance comes at the cost of a longer computation time, beten 0 and 2 orders of magnitude in our experiments."}]