Exploring data placement in racetrack memory based scratchpad memory

Abstract

Scratchpad Memory (SPM) has been widely adopted in various computing systems to improve performance of data access. Recently, non-volatile memory technologies (NVMs) have been employed for SPM design to improve its capacity and reduce its energy consumption. In this paper, we explore data allocation in SPM based on racetrack memory (RM), which is an emerging NVM with ultra-high storage density and fast access speed. Since a shift operation is needed to access data in RM, data allocation has an impact on performance of RM based SPM. Several allocation methods have been discussed and compared in this work. Especially, we addressed how to leverage genetic algorithm to achieve near-optimal data allocation.