Low-cost and efficient prediction hardware for tabular data using tiny classifier circuits

A typical machine learning development cycle maximizes performance during model training and then minimizes the memory and area footprint of the trained model for deployment on processing cores, graphics processing units, microcontrollers or custom hardware accelerators. However, this becomes increasingly difficult as machine learning models grow larger and more complex. Here we report a methodology for automatically generating predictor circuits for the classification of tabular data. The approach offers comparable prediction performance to conventional machine learning techniques as substantially fewer hardware resources and power are used. We use an evolutionary algorithm to search over the space of logic gates and automatically generate a classifier circuit with maximized training prediction accuracy, which consists of no more than 300 logic gates. When simulated as a silicon chip, our tiny classifiers use 8-18 times less area and 4-8 times less power than the best-performing machine learning baseline. When implemented as a low-cost chip on a flexible substrate, they occupy 10-75 times less area, consume 13-75 times less power and have 6 times better yield than the most hardware-efficient ML baseline. A graph-based genetic programming method can be used to automatically generate small and energy-efficient circuits from tabular data for machine learning classification tasks.