FPGA-Accelerated Causal Discovery with Conditional Independence Test Prioritization
Ce Guo, Diego Cupello, Wayne W. Luk, Joshua Levine, Alexander Warren, Peter Brookes · 2023
Causal discovery is a data mining approach that finds causal relations between variables from data. Causal discovery algorithms are computationally demanding when the data set has a high dimensionality or a large sample size. A promising way to expedite causal discovery is by utilizing FPGAs, but a significant drawback is that FPGA designs become inefficient when the on-chip memory cannot store the entire data set. This paper proposes Conditional Independence Test Prioritization (CITP), a novel approach that overcomes this limitation and enables fast FPGA-based causal discovery for large datasets with comparable speed and adequate accuracy to state-of-the-art methods. The main idea behind CITP is to design a workflow that allows a small subset of data to be stored in on-chip memory for prioritizing conditional independence tests. The paper provides experimental results that demonstrate the effectiveness of CITP in terms of both accuracy and speed. Our experiments show that for specific datasets, the proposed approach can respectively be 79 times, 2.6 times and 2.1 times faster than current CPU, GPU and FPGA designs.