Modern Hopfield-Heuristic Physical Computing Architecture for Diabetes Prediction

The classical Hopfield network exhibits the ability to solve specific combinatorial optimization problems; however, its inherent storage capacity constrains its accuracy in multi-class classification tasks. In contrast, the modern Hopfield algorithm, which leverages exponential functions, significantly enhances storage capacity and accelerates convergence. This paper presents a novel CMOS-integrable physical computing system that exploits the expanded capacity of modern Hopfield networks. By optimizing network architecture and hidden node circuits, the proposed hardware system effectively stores multiple patterns and retrieves the one that best matches a given input state. Experimental results on a binary diabetes prediction dataset demonstrate that the proposed hardware system achieves 91% accuracy. The system’s CMOS compatibility makes it a promising candidate for embedded health monitoring applications.