Binary Neural Network

Binary neural networks (BNNs) represent a significant advancement in deep learning by using only one bit to represent network weights and activations, drastically reducing computational cost and memory footprint compared to full-precision networks. Current research focuses on improving BNN training efficiency through techniques like cyclic precision training and novel optimization algorithms (e.g., variants of stochastic gradient descent), as well as exploring advanced architectures such as those based on UNet, Vision Transformers, and high-resolution networks. The resulting efficiency gains are particularly impactful for resource-constrained applications like edge computing, mobile devices, and embedded systems, enabling the deployment of powerful deep learning models in previously inaccessible environments.