CausaLearn: Automated Framework for Scalable Streaming-based Causal Bayesian Learning using FPGAs

Abstract

This paper proposes CausaLearn, the first automated framework that enables real-time and scalable approximation of Probability Density Function (PDF) in the context of causal Bayesian graphical models. CausaLearn targets complex streaming scenarios in which the input data evolves over time and independence cannot be assumed between data samples (e.g., continuous time-varying data analysis). Our framework is devised using a HW/SW co-design approach. We provide the first implementation of Hamiltonian Markov Chain Monte Carlo on FPGA that can efficiently sample from the steady state probability distribution at scales while considering the correlation between the observed data. CausaLearn is customizable to the limits of the underlying resource provisioning in order to maximize the effective system throughput. It uses physical profiling to abstract high-level hardware characteristics. These characteristics are integrated into our automated customization unit in order to tile, schedule, and batch the PDF approximation workload corresponding to the pertinent platform resources and constraints. We benchmark the design performance for analyzing various massive time-series data on three FPGA platforms with different computational budgets. Our extensive evaluations demonstrate up to two orders-of-magnitude runtime and energy improvements compared to the best-known prior solution. We provide an accompanying API that can be leveraged by data scientists and practitioners to automate and abstract hardware design optimization.