Can Wear-Aware Memory Allocation be Intelligent?

Many non-volatile memories (NVM) suffer from a severe reducedcell endurance and therefore require wear-leveling. Heap memory,as one segment, which potentially is mapped to a NVM, faces astrong application dependent characteristic regarding the amountof memory accesses and allocations. A simple deterministic strategyfor wear leveling of the heap may suffer when the available actionspace becomes too large. Therefore, we investigate the employmentof a reinforcement learning agent as a substitute for such a strategyin this paper. The agent's objective is to learn a strategy, which isoptimal with respect to the total memory wear out. We concludethis work with an evaluation, where we compare the deterministicstrategy with the proposed agent. We report that our proposedagent outperforms the simple deterministic strategy in several cases.However, we also report further optimization potential in the agentdesign and deployment.