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- W176952151 abstract "In this work we present neural network training algorithms, which are based on the differential evolution (DE) strategies introduced by Storn and Price [Journal of Global Optimization. 11:341–359, 1997]. These strategies are applied to train neural networks with 3–bit integer weights. Integer weight neural networks are better suited for hardware implementation than their real weight analogous. Moreover, we constrain the weights and biases in the range [−3, 3], thus, they can be represented by just 3 bits. This property reduces the amount of memory required and simplifies the digital multiplication operation. Our intention is to present a broad picture of the behavior of this class of evolution algorithms in this difficult task. Simulation results from classical benchmarks show that these methods are promising, fast, and reliable." @default.
- W176952151 created "2016-06-24" @default.
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- W176952151 date "2010-01-01" @default.
- W176952151 modified "2023-09-27" @default.
- W176952151 title "Training Neural Networks with 3–bit Integer Weights" @default.
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