探索将3D XPoint用作存内计算加速器的可行性
Exploring the Feasibility of Using 3D XPoint as an In-Memory Computing Accelerator
摘要：
本文介绍了如何将3D XPoint存储器阵列用作存内计算加速器。我们首先表明，阈值矩阵向量乘法（TMVM），在许多应用程序，包括机器学习的基本计算内核，可以在一个3D XPoint阵列中实现，而不需要数据离开阵列进行处理。利用TMVM的实现，我们讨论了一个二进制神经推理机的实现。我们讨论了核心概念的应用，以解决系统可扩展性等问题，其中我们连接多个3D XPoint阵列，以及电源完整性，其中我们分析了金属线对噪声裕度的寄生效应。为了确保在此实现过程中的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效应。我们量化了寄生效应对限制3D XPoint阵列的大小和配置的影响，并估计了3D XPoint子阵列的最大可接受大小。
英文摘要：
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.
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