A methodology — called auto tiny classifiers — is proposed to directly generate predictor circuits for the classification of tabular data, searching over the space of combinational logic using an evolutionary algorithm to maximize training prediction accuracy. Prediction performance is comparable to typical machine learning methods, but substantially fewer hardware resources and power are required.
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This is a summary of: Iordanou, K. et al. Low-cost and efficient prediction hardware for tabular data using tiny classifier circuits. Nat. Electron. https://doi.org/10.1038/s41928-024-01157-5 (2024).
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Tiny classifier circuits as accelerators for classification of tabular data. Nat Electron 7, 334–335 (2024). https://doi.org/10.1038/s41928-024-01166-4
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DOI: https://doi.org/10.1038/s41928-024-01166-4
