High Bandwidth Memory

High-bandwidth memory (HBM) is crucial for accelerating computationally intensive applications like large language model (LLM) inference and high-resolution image processing, addressing the bottleneck of data transfer between memory and processing units. Current research focuses on optimizing HBM usage in various deep learning architectures, including transformers and convolutional neural networks, through techniques like efficient memory management algorithms, model compression (e.g., top-k selection), and novel hardware designs that minimize data movement. These advancements are vital for enabling real-time performance in applications ranging from medical image analysis to AI-powered super-resolution, ultimately impacting the efficiency and scalability of numerous AI systems.