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A breakdown of a benchmark score is how much each aspect of the system performance affects the score. Existing methods require internal analysis on the benchmarking program and then involve the following problems: (1) require a certain amount of labor for code analysis, profiling, simulation, and so on and (2) require the benchmarking program itself. In this paper, we present a method for breaking down a benchmark score without internal analysis of the benchmarking program. The method utilizes regression analysis of benchmark scores on a number of systems. Experimental results with 3 benchmarks on 15 Android smartphones showed that our method could break down those benchmark scores even though there is room for improvement in accuracy.
Domain-specific software and hardware co-design is encouraging as it is much easier to achieve efficiency for fewer tasks. Agile domain-specific benchmarking speeds up the process as it provides not only relevant design inputs but also relevant metri
Lattice Boltzmann methods (LBM) are an important part of current computational fluid dynamics (CFD). They allow easy implementations and boundary handling. However, competitive time to solution not only depends on the choice of a reasonable method, b
The increasing attention on deep learning has tremendously spurred the design of intelligence processing hardware. The variety of emerging intelligence processors requires standard benchmarks for fair comparison and system optimization (in both softw
Recent advancements in ultra-low-power machine learning (TinyML) hardware promises to unlock an entirely new class of smart applications. However, continued progress is limited by the lack of a widely accepted benchmark for these systems. Benchmarkin
This report presents the design of the Scope infrastructure for extensible and portable benchmarking. Improvements in high- performance computing systems rely on coordination across different levels of system abstraction. Developing and defining accu