Spin-torque transfer magnetic random access memory (STT-MRAM) is a promising emerging non-volatile memory (NVM) technology with wide applications. However, the data recovery of STT-MRAM is affected by the diversity of channel raw bit error rate (BER) across different dies caused by process variations, as well as the unknown resistance offset due to temperature change. Therefore, it is critical to develop effective decoding algorithms of error correction codes (ECCs) for STT-MRAM. In this article, we first propose a neural bit-flipping (BF) decoding algorithm, which can share the same trellis representation as the state-of-the-art neural decoding algorithms, such as the neural belief propagation (NBP) and neural offset min-sum (NOMS) algorithm. Hence, a neural network (NN) decoder with a uniform architecture but different NN parameters can realize all these neural decoding algorithms. Based on such a unified NN decoder architecture, we further propose a novel deep-learning (DL)-based adaptive decoding algorithm whose decoding complexity can be adjusted according to the change of the channel conditions of STT-MRAM. Extensive experimental evaluation results demonstrate that the proposed neural decoders can greatly improve the performance over the standard decoders, with similar decoding latency and energy consumption. Moreover, the DL-based adaptive decoder can work well over different channel conditions of STT-MRAM irrespective of the unknown resistance offset, with a 50% reduction of the decoding latency and energy consumption compared to the fixed decoder.

翻译：自旋转移矩磁随机存储器（STT-MRAM）是一种具有广泛应用前景的新型非易失性存储器技术。然而，STT-MRAM的数据恢复受到工艺偏差导致的裸片间原始信道误码率差异以及温度变化引起的未知电阻偏移的影响。因此，开发适用于STT-MRAM的有效纠错码解码算法至关重要。本文首先提出一种神经比特翻转解码算法，该算法可与现有先进的神经解码算法（如神经置信传播算法和神经偏移最小和算法）共享相同的网格图表示。因此，一个具有统一架构但不同神经网络参数的神经网络解码器可实现所有这些神经解码算法。基于这种统一的神经网络解码器架构，我们进一步提出一种新型的基于深度学习的自适应解码算法，其解码复杂度可根据STT-MRAM信道条件的变化进行调整。大量实验评估结果表明，所提出的神经解码器在解码延迟和能耗相近的情况下，性能较标准解码器有显著提升。此外，基于深度学习的自适应解码器能在不同信道条件下稳定工作，且不受未知电阻偏移的影响，与固定解码器相比，其解码延迟和能耗降低了50%。