Gated Recurrent Units (GRUs) are a type of recurrent neural network (RNN) architecture designed to efficiently process sequential data. Introduced in 2014, GRUs simplify the structure of Long Short-Term Memory (LSTM) networks while retaining their core functionality. They utilize two primary gates: the reset gate and the update gate, which help manage the flow of information and mitigate issues like the vanishing gradient problem. This makes GRUs particularly effective for tasks involving long-term dependencies, such as natural language processing and time-series analysis, offering a balance between performance and computational efficiency.