Tiny classifiers offer high accuracy with minimal resources.

Researchers from the University of Manchester and Pragmatic Semiconductor have developed a novel method to generate tiny classifier circuits for the classification of tabular data using an evolutionary algorithm. Unlike traditional approaches, their method does not map to predefined machine learning models or hardware circuits. These tiny classifiers, composed of only a few hundred logic gates, achieve prediction accuracies comparable to state-of-the-art machine learning classifiers while using significantly fewer hardware resources and power. In simulations and real low-cost integrated circuits, these classifiers demonstrated 8-75 times less area and 4-75 times less power consumption compared to conventional methods. Potential applications include smart packaging, monitoring systems, and low-cost near-sensor computing.