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This repository contains the latest publicly available results from my research in Approximate Computing techniques applied to hardware realizations of Decision Tree Models.

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Approximate Computing applied to Decision Tree learning

The goal of this project is to develop approximate arithmetic and comparison blocks, as well as workflows, to be applied in Decision Tree model construction and usage, focusing on low-power operation.

This repo contains files used in research developed in the Embedded Computing Lab at UFSC (ECL/UFSC), as part of programs in Scientific and Technological Innovation Initiation (PIBITI/CNPq 2020-2021). Currently being continued as a final project for a B.Sc. in Computer Science at UFSC.

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This repository contains the latest publicly available results from my research in Approximate Computing techniques applied to hardware realizations of Decision Tree Models.

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