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- W3089980319 abstract "The use of Machine Learning techniques in battery-powered devices has increased in recent years. Therefore, evaluating power-accuracy trade-offs of inference models has become important. This work explores decision trees architectures, analyzing the effects of model complexity and approximation in power and accuracy. By quantizing the inputs to limited widths, we increased the accuracy of the models in up to 8.7% compared to precise cases. In terms of power, the variation in model complexity was more significant than in input width, as we obtained reductions of up to 97% by decreasing the tree depth, compared to 88% when decreasing the width." @default.
- W3089980319 created "2020-10-08" @default.
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- W3089980319 date "2020-10-01" @default.
- W3089980319 modified "2023-09-26" @default.
- W3089980319 title "VLSI Design of Tree-Based Inference for Low-Power Learning Applications" @default.
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- W3089980319 doi "https://doi.org/10.1109/iscas45731.2020.9180704" @default.
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