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Binary Perception in Neural Networks: A Computational Efficiency Framework

This paper examines binary perception as a foundational approach to neural network training, demonstrating how discrete information processing can enhance computational efficiency, reduce training overhead, and improve model interpretability. Through empirical analysis and theoretical frameworks, we establish that binary-based perception systems offer significant advantages over continuous-value approaches in resource-constrained environments while maintaining competitive accuracy across diverse applications.