Exploring the Feasibility of Using 3D XPoint as an In-Memory Computing Accelerator

TL;DR

This paper investigates using 3D XPoint memory arrays as in-memory computing accelerators, demonstrating their capability to perform matrix-vector multiplication and neural inference, while analyzing scalability and power integrity challenges.

Contribution

It introduces the concept of implementing thresholded matrix-vector multiplication within 3D XPoint arrays and explores system scalability and power integrity considerations.

Findings

3D XPoint arrays can perform TMVM without data leaving the array.

Power integrity is affected by parasitic effects of metal lines.

Maximum size of a 3D XPoint subarray is estimated based on parasitic analysis.

Abstract

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.