In-Memory Principal Component Analysis by Analogue Closed-Loop Eigendecomposition

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 securing $\times 10^{4}$ energy and $\times 10^{2\div 4}$ area efficiency improvements. These results support IMC as a leading candidate architecture for energy-efficient ML accelerators.