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In-Memory Principal Component Analysis by Analogue Closed-Loop Eigendecomposition
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DOI：10.1109/TCSII.2023.3334958.png)
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Machine learning (ML) techniques such as principal component analysis (PCA) have become pivotal in enabling efficient processing of big data in an increasing number of applications. However, the data-intensive computation in PCA causes large energy consumption in conventional von Neumann computers. In-memory computing (IMC) significantly improves throughput and energy efficiency by eliminating the physical separation between memory and processing units. Here, we present a novel closed-loop IMC circuit to compute real eigenvalues and eigenvectors of a target matrix allowing IMC-based acceleration of PCA. We benchmark its performance against a commercial GPU, achieving comparable accuracy and throughput while simultaneously securingx10(4)energy andx10(2 divided by 4)area efficiency improvements. These results support IMC as a leading candidate architecture for energy-efficient ML accelerators.
Keyword:
In-memory computing
eigendecomposition
principal component analysis
machine learning
analog computing
期刊
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IF:
4.9
论文数:
8.8K
被引数:
2.5W
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