Abstract
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.
Original language | English |
---|---|
Title of host publication | MLCAD 2020 - Proceedings of the 2020 ACM/IEEE Workshop on Machine Learning for CAD |
Pages | 83-88 |
DOIs | |
Publication status | Published - 2020 |
Externally published | Yes |
Event | 2nd ACM/IEEE Workshop on Machine Learning for CAD, MLCAD 2020 - Virtual Event Duration: 16 Nov 2020 → 20 Nov 2020 Conference number: 2 |
Conference
Conference | 2nd ACM/IEEE Workshop on Machine Learning for CAD, MLCAD 2020 |
---|---|
Abbreviated title | MLCAD 2020 |
City | Virtual Event |
Period | 16/11/20 → 20/11/20 |