Learning the Error Features of Approximate Multipliers for Neural Network Applications
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DOI: 10.1109/tc.2023.3345163
Abstract: Approximate multipliers (AMs) have widely been investigated to pursue high-performance and energy-efficient hardware designs for error-tolerant applications, such as neural networks (NNs). The computing accuracy of an AM has been evaluated by using statistical error… read more here.
Keywords: approximate multipliers; based applications; error features; accuracy ... See more keywords