Study On Reconfigurable Transfer Functions Approach For Use In AI Architecture To Reduce Memory Requirements And Speeding Up Calculations

Abstract

Traditional neural networks rely on fixed activation functions for each neuron, limiting their ability to adapt to diverse data and tasks. This paper proposes Reconfigurable Transfer Functions (RTFs), a novel approach that dynamically adjusts activation functions within neurons based on specific conditions or during training. Unlike traditional methods, RTFs offer flexibility by enabling neurons to switch between different activation functions or modify their behavior. This adaptability has the potential to improve performance and generalization across various tasks. However, implementing RTFs introduces complexity and may require additional computational resources. We explore two potential approaches for achieving RTFs: adaptive activation functions and meta-learning techniques. This research investigates the potential benefits and trade-offs associated with RTFs, paving the way for more versatile and efficient neural networks.