Algorithmic level approximate computing for machine learning classifiers

Description

Efficiency, real-time operation and low-power consumption are the main requirements of embedded Machine Learning implementations. This paper proposes an approach for applying Algorithmic level Approximate Computing Techniques (ACTs) on two supervised machine learning algorithms. The proposed approach has been validated in two different applications: touch modality and image classification. Results demonstrate that the proposed approach improves the performance with a speedup up to 3.2× with an accuracy loss of less than 4.7%.

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