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Title: Energy scaling advantages of resistive memory crossbar based computation and its application to sparse coding

In this study, the exponential increase in data over the last decade presents a significant challenge to analytics efforts that seek to process and interpret such data for various applications. Neural-inspired computing approaches are being developed in order to leverage the computational properties of the analog, low-power data processing observed in biological systems. Analog resistive memory crossbars can perform a parallel read or a vector-matrix multiplication as well as a parallel write or a rank-1 update with high computational efficiency. For an N × N crossbar, these two kernels can be O(N) more energy efficient than a conventional digital memory-based architecture. If the read operation is noise limited, the energy to read a column can be independent of the crossbar size (O(1)). These two kernels form the basis of many neuromorphic algorithms such as image, text, and speech recognition. For instance, these kernels can be applied to a neural sparse coding algorithm to give an O(N) reduction in energy for the entire algorithm when run with finite precision. Sparse coding is a rich problem with a host of applications including computer vision, object tracking, and more generally unsupervised learning.
Authors:
 [1] ;  [1] ;  [1] ;  [1] ;  [1] ;  [1] ;  [1]
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Publication Date:
OSTI Identifier:
1236485
Report Number(s):
SAND--2015-9530J
Journal ID: ISSN 1662-453X; 607777
Grant/Contract Number:
AC04-94AL85000
Type:
Accepted Manuscript
Journal Name:
Frontiers in Neuroscience (Online)
Additional Journal Information:
Journal Name: Frontiers in Neuroscience (Online); Journal Volume: 9; Journal Issue: C; Journal ID: ISSN 1662-453X
Publisher:
Frontiers Research Foundation
Research Org:
Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org:
USDOE National Nuclear Security Administration (NNSA)
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING