Runtime Efficiency-Accuracy Tradeoff Using Configurable Floating Point Multiplier.
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems(2020)
摘要
Many applications, such as machine learning and sensor data analysis, are statistical in nature and can tolerate some level of inaccuracy in their computation. Approximate computing is a viable method to save energy and increase performance by controllably trading off energy for accuracy. In this paper, we propose a tiered approximate floating point multiplier, called CFPU, which significantly red...
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关键词
Graphics processing units,Hardware,Neural networks,Machine learning algorithms,Adders,Machine learning,Error analysis
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