Extending the life of NOR flash memory using a genetic algorithm

We present methods, observations and insights into the application of Evolu- tionary Algorithms(EA) to the problem of ash memory wear-out. In doing so we examine the union of two distinct cutting edge technologies: that of Non Volatile Memory(NVM) and that of EA, specifically the class of EAs known as Genetic Algorithms(GA). The complete adoption of ash memory for those applications that require non-volatile storage is inhibited by a small number of negative characteristics of ash, most notably wear-out and the data retention/endurance trade-off. This thesis describes how to build and validate an automated system that uses evolutionary search techniques to perform embodied evolution on hard silicon in order to find programming parameters that will reduce wear. We use the system to optimise the read, write and erase conditions of the device to enhance reliability. Since the exploration is done on actual silicon in real time, it is costly in both those terms. However, it provides a level of accuracy that could barely be approximated in simulation due to the complexity of the devices, the variance between storage elements and the sheer number of unknowns. We mitigate this cost with the use of small population methods and the structured inclusion of some acquired domain knowledge. Results are calculated on a per device basis, with derived solutions com- pared to baseline results for that device. They demonstrate an increase in endurance of up to 300% per device. A blueprint for future experimentation with sequential access, or NAND ash memory, is presented. Although there has been an embargo placed on this thesis, with the result that very little can be published from it, this work has had considerable impact, getting coverage in international popular science publications, as well as leading to research funding involving several institutions and companies.