Back to Search
Start Over
High-Dimensional Computing as a Nanoscalable Paradigm.
- Source :
- IEEE Transactions on Circuits & Systems. Part I: Regular Papers; Sep2017, Vol. 64 Issue 9, p2508-2521, 14p
- Publication Year :
- 2017
-
Abstract
- We outline a model of computing with high-dimensional (HD) vectors—where the dimensionality is in the thousands. It is built on ideas from traditional (symbolic) computing and artificial neural nets/deep learning, and complements them with ideas from probability theory, statistics, and abstract algebra. Key properties of HD computing include a well-defined set of arithmetic operations on vectors, generality, scalability, robustness, fast learning, and ubiquitous parallel operation, making it possible to develop efficient algorithms for large-scale real-world tasks. We present a 2-D architecture and demonstrate its functionality with examples from text analysis, pattern recognition, and biosignal processing, while achieving high levels of classification accuracy (close to or above conventional machine-learning methods), energy efficiency, and robustness with simple algorithms that learn fast. HD computing is ideally suited for 3-D nanometer circuit technology, vastly increasing circuit density and energy efficiency, and paving a way to systems capable of advanced cognitive tasks. [ABSTRACT FROM PUBLISHER]
- Subjects :
- COMPUTING platforms
NANOTECHNOLOGY
COMPUTER systems
Subjects
Details
- Language :
- English
- ISSN :
- 15498328
- Volume :
- 64
- Issue :
- 9
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Circuits & Systems. Part I: Regular Papers
- Publication Type :
- Periodical
- Accession number :
- 125895727
- Full Text :
- https://doi.org/10.1109/TCSI.2017.2705051