Accelerating Hyperdimensional Classifier on Multiple GPUs

Zheming Jin; Hal Finkel

doi:10.1109/CLUSTER.2019.8891039

Accelerating Hyperdimensional Classifier on Multiple GPUs

Zheming Jin
, Hal Finkel

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

Among brain-inspired computing paradigms, hyperdimensional (HD) computing is based on mathematical properties of high-dimensional spaces which show remarkable agreement with brain-controlled behaviors [1]. In [2], the authors present an HD classifier for the task of identifying the language of text samples based on letter N-grams. They describe a computing architecture in which an encoding module generates a hypervector for each text sample and a search module compares the generated vector with a set of trained hypervectors. They provide an open-source implementation of their HD classifier written in hardware description language (HDL). In addition, they implemented the classifier with the 65-nm technology library, and evaluated the efficiency and accuracy of the classifier.

Original language	English
Title of host publication	Proceedings - 2019 IEEE International Conference on Cluster Computing, CLUSTER 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728147345
DOIs	https://doi.org/10.1109/CLUSTER.2019.8891039
State	Published - Sep 2019
Externally published	Yes
Event	2019 IEEE International Conference on Cluster Computing, CLUSTER 2019 - Albuquerque, United States Duration: Sep 23 2019 → Sep 26 2019

Publication series

Name	Proceedings - IEEE International Conference on Cluster Computing, ICCC
Volume	2019-September
ISSN (Print)	1552-5244

Conference

Conference	2019 IEEE International Conference on Cluster Computing, CLUSTER 2019
Country/Territory	United States
City	Albuquerque
Period	09/23/19 → 09/26/19

Funding

The research was supported by the U.S. Department of Energy, Office of Science, under contract DE AC02 06CH11357 and made use of the Argonne Leadership Computing Facility, a DOE Office of Science User Facility

Access to Document

10.1109/CLUSTER.2019.8891039

Cite this

Jin, Z., & Finkel, H. (2019). Accelerating Hyperdimensional Classifier on Multiple GPUs. In Proceedings - 2019 IEEE International Conference on Cluster Computing, CLUSTER 2019 Article 8891039 (Proceedings - IEEE International Conference on Cluster Computing, ICCC; Vol. 2019-September). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CLUSTER.2019.8891039

@inproceedings{f9f4741bd94d41fb814befd8c9b58b1f,

title = "Accelerating Hyperdimensional Classifier on Multiple GPUs",

abstract = "Among brain-inspired computing paradigms, hyperdimensional (HD) computing is based on mathematical properties of high-dimensional spaces which show remarkable agreement with brain-controlled behaviors [1]. In [2], the authors present an HD classifier for the task of identifying the language of text samples based on letter N-grams. They describe a computing architecture in which an encoding module generates a hypervector for each text sample and a search module compares the generated vector with a set of trained hypervectors. They provide an open-source implementation of their HD classifier written in hardware description language (HDL). In addition, they implemented the classifier with the 65-nm technology library, and evaluated the efficiency and accuracy of the classifier.",

author = "Zheming Jin and Hal Finkel",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE International Conference on Cluster Computing, CLUSTER 2019 ; Conference date: 23-09-2019 Through 26-09-2019",

year = "2019",

month = sep,

doi = "10.1109/CLUSTER.2019.8891039",

language = "English",

series = "Proceedings - IEEE International Conference on Cluster Computing, ICCC",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "Proceedings - 2019 IEEE International Conference on Cluster Computing, CLUSTER 2019",

}

Jin, Z & Finkel, H 2019, Accelerating Hyperdimensional Classifier on Multiple GPUs. in Proceedings - 2019 IEEE International Conference on Cluster Computing, CLUSTER 2019., 8891039, Proceedings - IEEE International Conference on Cluster Computing, ICCC, vol. 2019-September, Institute of Electrical and Electronics Engineers Inc., 2019 IEEE International Conference on Cluster Computing, CLUSTER 2019, Albuquerque, United States, 09/23/19. https://doi.org/10.1109/CLUSTER.2019.8891039

Accelerating Hyperdimensional Classifier on Multiple GPUs. / Jin, Zheming; Finkel, Hal.
Proceedings - 2019 IEEE International Conference on Cluster Computing, CLUSTER 2019. Institute of Electrical and Electronics Engineers Inc., 2019. 8891039 (Proceedings - IEEE International Conference on Cluster Computing, ICCC; Vol. 2019-September).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Accelerating Hyperdimensional Classifier on Multiple GPUs

AU - Jin, Zheming

AU - Finkel, Hal

PY - 2019/9

Y1 - 2019/9

N2 - Among brain-inspired computing paradigms, hyperdimensional (HD) computing is based on mathematical properties of high-dimensional spaces which show remarkable agreement with brain-controlled behaviors [1]. In [2], the authors present an HD classifier for the task of identifying the language of text samples based on letter N-grams. They describe a computing architecture in which an encoding module generates a hypervector for each text sample and a search module compares the generated vector with a set of trained hypervectors. They provide an open-source implementation of their HD classifier written in hardware description language (HDL). In addition, they implemented the classifier with the 65-nm technology library, and evaluated the efficiency and accuracy of the classifier.

AB - Among brain-inspired computing paradigms, hyperdimensional (HD) computing is based on mathematical properties of high-dimensional spaces which show remarkable agreement with brain-controlled behaviors [1]. In [2], the authors present an HD classifier for the task of identifying the language of text samples based on letter N-grams. They describe a computing architecture in which an encoding module generates a hypervector for each text sample and a search module compares the generated vector with a set of trained hypervectors. They provide an open-source implementation of their HD classifier written in hardware description language (HDL). In addition, they implemented the classifier with the 65-nm technology library, and evaluated the efficiency and accuracy of the classifier.

UR - https://www.scopus.com/pages/publications/85075270695

U2 - 10.1109/CLUSTER.2019.8891039

DO - 10.1109/CLUSTER.2019.8891039

M3 - Conference contribution

AN - SCOPUS:85075270695

T3 - Proceedings - IEEE International Conference on Cluster Computing, ICCC

BT - Proceedings - 2019 IEEE International Conference on Cluster Computing, CLUSTER 2019

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 IEEE International Conference on Cluster Computing, CLUSTER 2019

Y2 - 23 September 2019 through 26 September 2019

ER -

Accelerating Hyperdimensional Classifier on Multiple GPUs

Abstract

Publication series

Conference

Funding

Access to Document

Other files and links

Fingerprint

Cite this