Memory Hierarchy

Memory hierarchy optimizes data access speed and efficiency by organizing memory into layers with varying access times and capacities, aiming to minimize latency and energy consumption. Current research focuses on adapting memory hierarchies to the demands of deep neural networks (DNNs), particularly for computationally intensive tasks like convolutional and self-attention operations, employing techniques like configurable memory frameworks and optimized data layouts to improve performance. These advancements are crucial for accelerating DNN inference and training, particularly in resource-constrained environments like mobile devices and edge computing, impacting both the efficiency of machine learning applications and the design of future hardware architectures.