Exploration of Memory Hierarchy, Thermodynamic Costs, and Edge Computing in Computational Systems

The structure of computational systems involves a hierarchical memory arrangement where the CPU resides at the top, executing tasks and needing data stored at the lower levels. Retrieving data from lower memory levels incurs significant thermodynamic costs, leading to inefficiency in deep neural networks. The concept of memory compute, tied to edge computing, is crucial for systems like robotics and autonomous vehicles that require on-device intelligence due to limited power and communication constraints. In-memory computing, exemplified by neuromorphic chips, minimizes energy usage and is pivotal for edge computing scenarios. Achieving the lower limit on thermodynamic costs through in-memory computing aligns with the principle of variational free energy minimization, optimizing systems to be thermodynamically efficient and information-theoretically optimal. This approach is especially beneficial for edge computing applications, like advanced robotic systems, emphasizing the significance of memory compute in enhancing computational efficiency across various domains.