Binary Neural

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 by addressing issues like weight sign flipping ("silent weights"), optimizing scale factor calculation through techniques such as recurrent bilinear optimization, and enhancing training stability to mitigate weight oscillation. These advancements, along with explorations of hardware acceleration using technologies like superconducting Josephson junctions, are driving the development of highly efficient BNNs for resource-constrained applications in areas such as mobile devices and healthcare.