Memory Allocation

Memory allocation in deep learning, particularly for large language models (LLMs) and dynamic neural networks (DNNs), is a critical area of research focused on optimizing resource utilization and minimizing execution latency. Current efforts concentrate on techniques like fine-grained activation memory management, static optimization of dynamic tensor shapes, and adaptive memory allocation strategies for continual learning, often employing novel algorithms and model architectures to improve efficiency. These advancements are crucial for enabling the training and deployment of increasingly complex models on resource-constrained hardware, impacting the scalability and performance of various applications from natural language processing to real-time object detection. Efficient memory management is essential for reducing energy consumption and improving the overall speed and practicality of deep learning systems.