Speeding up crossbar resistive memory by exploiting in-memory data patterns

Abstract

Resistive Memory (ReRAM) has emerged as a promising non-volatile memory technology that may replace a significant portion of DRAM in future computer systems. ReRAM has many advantages such as high density, low standby power and good scalability. ReRAM, when adopting crossbar architecture, has the smallest 4F2 planar cell size, which is ideal for constructing dense memory with large capacity. However, crossbar cell structure suffers from large sneak leakage and IR drop on long wires. To ensure operation reliability, ReRAM writes, in particular, RESET operations, conservatively use the worst-case access latency of all cells in ReRAM arrays, which leads to significant performance degradation and dynamic energy waste. In this paper, we study the correlation between the RESET latency and the number of cells in low resistant state (LRS) along bitlines, and propose to dynamically speed up ReRAM RESET operations for the rows that have small numbers of LRS cells. We leverage the intrinsic in-memory processing capability of ReRAM crossbar and propose a low overhead runtime profiler that effectively tracks the data patterns in different bitlines. To achieve further RESET latency reduction, we employ data compression and row address dependent data layout to reduce LRS cells on bitlines. The experimental results show that, on average, our design improves system performance by 20.5% and 14.2%, and reduces memory dynamic energy by 15.7% and 7.6%, compared to the baseline and the state-of-the-art crossbar design.