This paper describes how 3D XPoint memory arrays can be used as in-memory computing accelerators. We first show that thresholded matrix-vector multiplication (TMVM), the fundamental computational kernel in many applications including machine learning, can be implemented within a 3D XPoint array without requiring data to leave the array for processing. Using the implementation of TMVM, we then discuss the implementation of a binary neural inference engine. We discuss the application of the core concept to address issues such as system scalability, where we connect multiple 3D XPoint arrays, and power integrity, where we analyze the parasitic effects of metal lines on noise margins. To assure power integrity within the 3D XPoint array during this implementation, we carefully analyze the parasitic effects of metal lines on the accuracy of the implementations. We quantify the impact of parasitics on limiting the size and configuration of a 3D XPoint array, and estimate the maximum acceptable size of a 3D XPoint subarray.

中文翻译：

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探索使用 3D XPoint 作为内存计算加速器的可行性

本文介绍了如何将 3D XPoint 内存阵列用作内存计算加速器。我们首先展示了阈值矩阵向量乘法 (TMVM)，包括机器学习在内的许多应用程序中的基本计算内核，可以在 3D XPoint 阵列中实现，而无需数据离开阵列进行处理。使用 TMVM 的实现，我们然后讨论二元神经推理引擎的实现。我们讨论了核心概念在解决系统可扩展性（连接多个 3D XPoint 阵列）和电源完整性（分析金属线对噪声容限的寄生效应）等问题上的应用。为了在此实施过程中确保 3D XPoint 阵列内的电源完整性，我们仔细分析了金属线对实现精度的寄生影响。我们量化了寄生效应对限制 3D XPoint 阵列大小和配置的影响，并估计了 3D XPoint 子阵列的最大可接受尺寸。