Hechen Wang is a research scientist for Intel Labs with interests in mixed-signal circuits, data converters, digital frequency synthesizers, wireless communication systems, and analog/mixed-signal compute-in-memory for AI applications. The fundamental building block of computer memory is the memory cell; an electronic circuit that stores binary information. In the conventional approach to data processing, the data resides on a hard disk in the system or attached by a network. When needed, it's called into the local system memory, or RAM, and then moves to the CPU. The lengthy process is relatively inefficient, so researchers began to seek an alternative.

Abstract: Matrix factorization, one of the most popular methods in machine learning, has recently benefited from introducing non-linearity in prediction tasks using tropical semiring. The non-linearity enables a better fit to extreme values and distributions, thus discovering high-variance patterns that differ from those found by standard linear algebra. However, the optimization process of various tropical matrix factorization methods is slow. In our work, we propose a new method FastSTMF based on Sparse Tropical Matrix Factorization (STMF), which introduces a novel strategy for updating factor matrices that results in efficient computational performance. We evaluated the efficiency of FastSTMF on synthetic and real gene expression data from the TCGA database, and the results show that FastSTMF outperforms STMF in both accuracy and running time.