Performance Analysis and Code Design for Resistive Random-Access Memory Using Channel Decomposition Approach

TL;DR

This paper introduces a new framework for analyzing and designing codes for ReRAM memory, addressing the sneak path problem by decomposing the memory channel into stationary components and applying sparse-graph codes.

Contribution

It proposes a novel channel decomposition method for ReRAM, enabling finite-length performance bounds and practical code design close to capacity.

Findings

Finite-length performance bounds derived for ReRAM channels

Sparse-graph codes designed with density evolution approach

Codes achieve near-capacity performance in simulations

Abstract

A novel framework for performance analysis and code design is proposed to address the sneak path (SP) problem in resistive random-access memory (ReRAM) arrays. The main idea is to decompose the ReRAM channel, which is both non-ergodic and data-dependent, into multiple stationary memoryless channels. A finite-length performance bound is derived by analyzing the capacity and dispersion of these stationary memoryless channels. Furthermore, leveraging this channel decomposition, a practical sparse-graph code design is proposed using density evolution. The obtained channel codes are not only asymptotic capacity approaching but also close to the derived finite-length performance bound.