Sneak Path Interference-Aware Adaptive Detection and Decoding for Resistive Memory Arrays
Panpan Li, Kui Cai, Guanghui Song, and Zhen Mei

TL;DR
This paper introduces an adaptive detection and decoding scheme for ReRAM arrays that accounts for sneak path interference, improving data recovery performance through a novel channel model and array-specific quantizer design.
Contribution
It proposes a new quantized channel model for ReRAM, along with array-level and column-level SPI-aware adaptive detection and decoding methods that outperform existing approaches.
Findings
Approaching ideal performance with only three quantization bits.
Array-level scheme outperforms the non-adaptive approach.
Column-level scheme further improves detection accuracy.
Abstract
Resistive random-access memory (ReRAM) is an emerging non-volatile memory technology for high-density and high-speed data storage. However, the sneak path interference (SPI) occurred in the ReRAM crossbar array seriously affects its data recovery performance. In this letter, we first propose a quantized channel model of ReRAM, based on which we design both the one-bit and multi-bit channel quantizers by maximizing the mutual information of the channel. A key channel parameter that affects the quantizer design is the sneak path occurrence probability (SPOP) of the memory cell. We first use the average SPOP calculated statistically to design the quantizer, which leads to the same channel detector for different memory arrays. We then adopt the SPOP estimated separately for each memory array for the quantizer design, which is generated by an effective channel estimator and through an…
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Taxonomy
TopicsAdvanced Memory and Neural Computing · Advanced Data Storage Technologies · Distributed systems and fault tolerance
