Exploiting multiple write modes of Nonvolatile main memory in embedded systems

Existing Nonvolatile Memories (NVMs) have many attractive features to be the main memory of embedded systems. These features include low power, high density, and better scalability. Recently, Multilevel Cell (MLC) NVM has gained more and more popularity as it can provide a higher density than the traditional Single-Level Cell (SLC) NVM. However, there are also drawbacks in MLC NVM, namely, limited write endurance and expensive write operation. These two drawbacks have to be overcome before MLC NVM can be practically adopted as the main memory. In MLC Nonvolatile Main Memory (NVMM), two different types of write operations with very diverse data retention times are allowed. The first type maintains data for years but takes a longer time to write and is detrimental to the endurance. The second type maintains data for a short period but takes a shorter time to write. By observing that much of the data written to main memory is temporary and does not need to last long during the execution of a program, in this article, we propose novel task scheduling and write operation selection algorithms to improve MLC NVMM endurance and program efficiency. An Integer Linear Programming (ILP) formulation is first proposed to obtain optimal results. Since ILP takes exponential time to solve, we also propose the Multiwrite Mode-Aware Scheduling (MMAS) algorithm to achieve a near-optimal solution in polynomial time. Additionally, the Dynamical Memory Block Screening (DMS) algorithm is proposed to achieve wear leveling. The experimental results demonstrate that the proposed techniques can greatly improve the lifetime of the MLC NVMM as well as the efficiency of the program.

Duke Scholars

Author Yiran Chen Electrical and Computer Engineering