Exploiting In-Memory Data Patterns for Performance Improvement on Crossbar Resistive Memory

Abstract

Resistive memory (ReRAM) has emerged as a promising nonvolatile 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. When adopting crossbar architecture, ReRAM cell can achieve the smallest theoretical size in fabrication, which is ideal for constructing dense memory with large capacity. However, crossbar cell structure suffers from a variety of reliability issues, which come from large voltage drops on long wires. To ensure operation reliability, ReRAM writes 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 article, we study the correlation between the ReRAM cell switching latency and the number of cells in low-resistance state (LRS) along bitlines, and propose to dynamically speed up write operations based on bitline data patterns, i.e., the number of LRS cells presented in bitlines. 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 write latency reduction, we employ data compression and row address dependent memory data layout to reduce the numbers of LRS cells on bitlines. Moreover, we further present two optimization techniques, i.e., selective profiling and fine-grained profiling, to mitigate energy overhead brought by bitline data patterns tracking. The experimental results show that, on average, our design improves system performance by 20.5% and 14.2%, and reduces memory dynamic energy by 20.3% and 12.6%, compared to the baseline and the state-of-the-art crossbar design, respectively.