Neural Network Training

Training large neural networks efficiently is a critical challenge in machine learning, focusing on minimizing memory usage, maximizing computational throughput, and improving scalability across diverse hardware architectures. Current research emphasizes optimizing training algorithms (like Adam and Shampoo), developing novel parallelization techniques (including pipeline and data parallelism across GPUs and CPUs), and exploring efficient data formats and quantization methods to reduce memory footprints. These advancements are crucial for enabling the training of increasingly complex models for various applications, ranging from natural language processing and image recognition to large-scale graph neural networks, while also addressing concerns about computational cost and energy consumption.