Low Rate ACCELERATION

Low-rate acceleration research focuses on developing efficient algorithms and hardware to speed up computations while maintaining accuracy, particularly in resource-constrained environments or for high-dimensional data. Current efforts concentrate on optimizing existing methods like stochastic gradient descent (SGD) and ADMM through techniques such as momentum, adaptive step sizes, and Anderson acceleration, as well as exploring novel architectures like power-of-two quantization and specialized hardware accelerators. These advancements are significant for various applications, including machine learning model training, image reconstruction (e.g., MRI, CT), and real-time processing of sensor data in autonomous systems, ultimately improving efficiency and reducing computational costs.