Journal
NANO LETTERS
Volume 17, Issue 5, Pages 3113-3118Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acs.nanolett.7b00552
Keywords
Unsupervised learning; principal component analysis; clustering; neuromorphic computing; artificial neural network; RRAM
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Funding
- AFOSR through MURI Grant [FA9550-12-1-0038]
- National Science Foundation (NSF) [CCF-1617315]
- Defense Advanced Research Program Agency (DARPA) [HR0011-13-2-0015]
- Samsung Scholarship
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Memristors have been considered as a leading candidate for a number of critical applications ranging from nonvolatile memory to non-Von Neumann computing systems. Feature extraction, which aims to transform input data from a high-dimensional space to a space with fewer dimensions, is an important technique widely used in machine learning and pattern recognition applications. Here, we experimentally demonstrate that memristor arrays can be used to perform principal component analysis, one of the most commonly used feature extraction techniques, through online, unsupervised learning. Using Sangers rule, that is, the generalized Hebbian algorithm, the principal components were obtained as the memristor conductances in the network after training. The network was then used to analyze sensory data from a standard breast cancer screening database with high classification success rate (97.1%).
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