Memory Optimization

Memory optimization is crucial for improving the efficiency and scalability of various computational tasks, particularly in deep learning and related fields. Current research focuses on developing novel algorithms and architectures to reduce memory consumption in large language models, deep neural networks (including transformers and spiking neural networks), and recommendation systems, often employing techniques like tiling, balanced workload optimization, and efficient data structures. These advancements are vital for enabling the training and deployment of increasingly complex models on resource-constrained devices (like microcontrollers in TinyML) and for accelerating the performance of large-scale applications, such as those in space operations and industry-scale recommendation systems.