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Title: A historical survey of algorithms and hardware architectures for neural-inspired and neuromorphic computing applications

Abstract

In this study, biological neural networks continue to inspire new developments in algorithms and microelectronic hardware to solve challenging data processing and classification problems. Here in this research, we survey the history of neural-inspired and neuromorphic computing in order to examine the complex and intertwined trajectories of the mathematical theory and hardware developed in this field. Early research focused on adapting existing hardware to emulate the pattern recognition capabilities of living organisms. Contributions from psychologists, mathematicians, engineers, neuroscientists, and other professions were crucial to maturing the field from narrowly-tailored demonstrations to more generalizable systems capable of addressing difficult problem classes such as object detection and speech recognition. Algorithms that leverage fundamental principles found in neuroscience such as hierarchical structure, temporal integration, and robustness to error have been developed, and some of these approaches are achieving world-leading performance on particular data classification tasks. Additionally, novel microelectronic hardware is being developed to perform logic and to serve as memory in neuromorphic computing systems with optimized system integration and improved energy efficiency. Key to such advancements was the incorporation of new discoveries in neuroscience research, the transition away from strict structural replication and towards the functional replication of neural systems, and the usemore » of mathematical theory frameworks to guide algorithm and hardware developments.« less

Authors:
 [1];  [1];  [1];  [1];  [1];  [1];  [1];  [1];  [2];  [1];  [1];  [1]
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  2. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Google X, Mountain View, CA (United States)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1340263
Alternate Identifier(s):
OSTI ID: 1397404
Report Number(s):
SAND-2016-12503J
Journal ID: ISSN 2212-683X; 649832
Grant/Contract Number:
AC04-94AL85000
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Biologically Inspired Cognitive Architectures
Additional Journal Information:
Journal Volume: 19; Journal Issue: C; Journal ID: ISSN 2212-683X
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; neuromorphic computing; algorithms; artificial neural networks; data-driven computing; machine learning; pattern recognition

Citation Formats

James, Conrad D., Aimone, James B., Miner, Nadine E., Vineyard, Craig M., Rothganger, Fredrick H., Carlson, Kristofor D., Mulder, Samuel A., Draelos, Timothy J., Faust, Aleksandra, Marinella, Matthew J., Naegle, John H., and Plimpton, Steven J.. A historical survey of algorithms and hardware architectures for neural-inspired and neuromorphic computing applications. United States: N. p., 2017. Web. doi:10.1016/j.bica.2016.11.002.
James, Conrad D., Aimone, James B., Miner, Nadine E., Vineyard, Craig M., Rothganger, Fredrick H., Carlson, Kristofor D., Mulder, Samuel A., Draelos, Timothy J., Faust, Aleksandra, Marinella, Matthew J., Naegle, John H., & Plimpton, Steven J.. A historical survey of algorithms and hardware architectures for neural-inspired and neuromorphic computing applications. United States. doi:10.1016/j.bica.2016.11.002.
James, Conrad D., Aimone, James B., Miner, Nadine E., Vineyard, Craig M., Rothganger, Fredrick H., Carlson, Kristofor D., Mulder, Samuel A., Draelos, Timothy J., Faust, Aleksandra, Marinella, Matthew J., Naegle, John H., and Plimpton, Steven J.. Wed . "A historical survey of algorithms and hardware architectures for neural-inspired and neuromorphic computing applications". United States. doi:10.1016/j.bica.2016.11.002. https://www.osti.gov/servlets/purl/1340263.
@article{osti_1340263,
title = {A historical survey of algorithms and hardware architectures for neural-inspired and neuromorphic computing applications},
author = {James, Conrad D. and Aimone, James B. and Miner, Nadine E. and Vineyard, Craig M. and Rothganger, Fredrick H. and Carlson, Kristofor D. and Mulder, Samuel A. and Draelos, Timothy J. and Faust, Aleksandra and Marinella, Matthew J. and Naegle, John H. and Plimpton, Steven J.},
abstractNote = {In this study, biological neural networks continue to inspire new developments in algorithms and microelectronic hardware to solve challenging data processing and classification problems. Here in this research, we survey the history of neural-inspired and neuromorphic computing in order to examine the complex and intertwined trajectories of the mathematical theory and hardware developed in this field. Early research focused on adapting existing hardware to emulate the pattern recognition capabilities of living organisms. Contributions from psychologists, mathematicians, engineers, neuroscientists, and other professions were crucial to maturing the field from narrowly-tailored demonstrations to more generalizable systems capable of addressing difficult problem classes such as object detection and speech recognition. Algorithms that leverage fundamental principles found in neuroscience such as hierarchical structure, temporal integration, and robustness to error have been developed, and some of these approaches are achieving world-leading performance on particular data classification tasks. Additionally, novel microelectronic hardware is being developed to perform logic and to serve as memory in neuromorphic computing systems with optimized system integration and improved energy efficiency. Key to such advancements was the incorporation of new discoveries in neuroscience research, the transition away from strict structural replication and towards the functional replication of neural systems, and the use of mathematical theory frameworks to guide algorithm and hardware developments.},
doi = {10.1016/j.bica.2016.11.002},
journal = {Biologically Inspired Cognitive Architectures},
number = C,
volume = 19,
place = {United States},
year = {Wed Jan 04 00:00:00 EST 2017},
month = {Wed Jan 04 00:00:00 EST 2017}
}

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