Adder Neural Network

Adder neural networks (AdderNets) replace computationally expensive multiplication operations in traditional neural networks with additions, aiming to improve energy efficiency and reduce hardware complexity, particularly for resource-constrained applications like edge AI. Current research focuses on optimizing AdderNet architectures through techniques like reinforcement learning to design efficient adder circuits and developing quantization methods to further enhance hardware efficiency without significant accuracy loss. These advancements hold significant promise for improving the performance and energy efficiency of various applications, from image classification and object detection to more general-purpose computing.