BNN Layer

Binary Neural Networks (BNNs) aim to drastically reduce the computational cost and memory footprint of deep learning models by representing weights and activations using only one bit. Current research focuses on improving BNN accuracy through techniques like optimized gradient approximation, novel activation functions, and adaptive quantization thresholds, often employing architectures such as ResNet and MobileNet. These advancements are significant for deploying deep learning on resource-constrained devices like embedded systems and mobile platforms, impacting both the efficiency of existing applications and enabling new possibilities in edge computing.