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LCA

RESOURCE

Publicly Accessible Repository
https://github.com/pnnl/LCA
https://doi.org/10.11578/dc.20230316.2

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Abstract

The Locally Competitive Algorithm (LCA) is a dynamical sparse solver that uses only local computations, allowing for massively parallel implementations on compatible neuromorphic architectures such as Intel's Loihi research chip. In this invention, we show how unsupervised dictionary learning with spiking LCA can be implemented on GPUs and Intel's Loihi research chip.
Developers:
Parpart, Gavin [1] ;  Watkins,, Yijing [1]
  1. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Release Date:
2023-03-16
Project Type:
Open Source, Publicly Available Repository
Software Type:
Scientific
Licenses:
BSD 2-clause "Simplified" License
Sponsoring Org.:
Code ID:
103196
Site Accession Number:
Battelle IPID 32667-E
Research Org.:
Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
Country of Origin:
United States

RESOURCE

Publicly Accessible Repository
https://github.com/pnnl/LCA
https://doi.org/10.11578/dc.20230316.2

SAVE / SHARE



PNNL Badge

Citation Formats

Parpart, Gavin, and Watkins,, Yijing. LCA. Computer Software. https://github.com/pnnl/LCA. USDOE. 16 Mar. 2023. Web. doi:10.11578/dc.20230316.2.
Parpart, Gavin, & Watkins,, Yijing. (2023, March 16). LCA. [Computer software]. https://github.com/pnnl/LCA. https://doi.org/10.11578/dc.20230316.2.
Parpart, Gavin, and Watkins,, Yijing. "LCA." Computer software. March 16, 2023. https://github.com/pnnl/LCA. https://doi.org/10.11578/dc.20230316.2.
@misc{ doecode_103196,
title = {LCA},
author = {Parpart, Gavin and Watkins,, Yijing},
abstractNote = {The Locally Competitive Algorithm (LCA) is a dynamical sparse solver that uses only local computations, allowing for massively parallel implementations on compatible neuromorphic architectures such as Intel's Loihi research chip. In this invention, we show how unsupervised dictionary learning with spiking LCA can be implemented on GPUs and Intel's Loihi research chip.},
doi = {10.11578/dc.20230316.2},
url = {https://doi.org/10.11578/dc.20230316.2},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20230316.2}},
year = {2023},
month = {mar}
}