Energy-Efficient Parametric Activation for Deep Hardware Learning Applications

The rapid advancement of deep learning applications in computer vision, natural language processing, and autonomous systems has intensified the demand for energy-efficient neural network hardware. In response to the limitations of conventional activation function implementations—such as high power consumption, complex analog-digital hybrid circuits, or large lookup tables—we propose a novel circuit design based on parallel three-bit comparators combined with a parameterized 16-bit floating-point multiplier. Unlike traditional designs, our approach enables flexible realization of both linear and non-linear activation functions while significantly reducing power and propagation delay. Through hierarchical comparator batching and critical path optimization techniques, the circuit achieves over 40% energy savings compared to baseline implementations without compromising functional accuracy. Additionally, our floating-point multiplier architecture enhances support for sub-threshold and negative value modeling, offering greater robustness and adaptability. This work contributes a scalable, low-power hardware solution suitable for deployment in energy-constrained edge AI systems, and establishes a foundation for future research in efficient neural network activation.