Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Exploring the Feasibility of Using 3D XPoint as an In-Memory Computing
  Accelerator   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.

12
The categories of this paper are: nucl-th
35
The categories of this paper are: mtrl-th
2
The categories of this paper are: math
3
The categories of this paper are: physics
19
The categories of this paper are: eess
21
The categories of this paper are: adap-org
8
The categories of this paper are: hep-ex
33
The categories of this paper are: plasm-ph
26
The categories of this paper are: bayes-an
0
The categories of this paper are: hep-ph
14
The categories of this paper are: math-ph
28
The categories of this paper are: alg-geom
32
The categories of this paper are: atom-ph
16
The categories of this paper are: stat
5
The categories of this paper are: gr-qc
15
The categories of this paper are: nucl-ex