Weight Binarization

Weight binarization is a model compression technique that reduces the memory footprint and computational cost of neural networks by representing weights as single bits (0 or 1), significantly speeding up inference. Current research focuses on applying this technique to various architectures, including recurrent neural networks (RNNs), vision transformers (ViTs), and convolutional neural networks (CNNs), often employing strategies like iterative training or group superposition binarization to mitigate accuracy loss. This approach holds significant promise for deploying large-scale models on resource-constrained devices and improving the efficiency of applications ranging from speech recognition to image processing.