NAND flash memory is a popular choice for storing a large number of model weights of an Artificial Neural Network (ANN) in many Internet of Things devices and edge computing applications. While being used as a weight storage device, the bit error rate of flash memory plays a significant role in the performance of the ANN application. In this paper, we propose two novel weight storage method in NAND flash memory, which will significantly suppress the effects of bit error rate on the ANN's model weights and its performance. The proposed weight storage methods utilize the NAND flash's page-to-page variability in favor of storing the model weights on more reliable pages. In order to demonstrate the benefit of the proposed method, we perform different reliability experiments on commercial flash memory chips containing the trained weight values of an ANN application. The experimental evaluation shows that the proposed method outperforms the traditional weight storage method and ensures prediction accuracy more than 90% even with a bit error rate exceeding 1% value.

中文翻译：

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NAND闪存作为人工神经网络权值存储器件的可靠性


NAND 闪存是许多物联网设备和边缘计算应用中存储大量人工神经网络 (ANN) 模型权重的流行选择。在用作权重存储设备时，闪存的误码率对 ANN 应用的性能起着重要作用。在本文中，我们提出了两种新颖的 NAND 闪存中的权重存储方法，该方法将显着抑制误码率对 ANN 模型权重及其性能的影响。所提出的权重存储方法利用 NAND 闪存的页间可变性，有利于将模型权重存储在更可靠的页面上。为了证明所提出方法的优点，我们在包含 ANN 应用训练权重值的商用闪存芯片上进行了不同的可靠性实验。实验评估表明，该方法优于传统的权值存储方法，即使在误码率超过1%的情况下，也能保证90%以上的预测精度。