Work-in-Progress: Toward a Robust, Reconfigurable Hardware Accelerator for Tree-Based Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Crary:2022:CASES,

• author = "Christopher Crary and Wesley Piard and Britton Chesley and Greg Stitt",
• booktitle = "2022 International Conference on Compilers, Architecture, and Synthesis for Embedded Systems (CASES)",
• title = "Work-in-Progress: Toward a Robust, Reconfigurable Hardware Accelerator for Tree-Based Genetic Programming",
• year = "2022",
• pages = "17--18",
• abstract = "Genetic programming (GP) is a general, broadly effective procedure by which computable solutions are constructed from high-level objectives. As with other machine-learning endeavors, one continual trend for GP is to exploit ever-larger amounts of parallelism. In this paper, we explore the possibility of accelerating GP by way of modern field-programmable gate arrays (FPGAs), which is motivated by the fact that FPGAs can sometimes leverage larger amounts of both function and data parallelism-common characteristics of GP- when compared to CPUs and GPUs. As a first step towards more general acceleration, we present a preliminary accelerator for the evaluation phase of {"}tree-based GP{"}-the original, and still popular, flavor of GP-for which the FPGA dynamically compiles programs of varying shapes and sizes onto a reconfigurable function tree pipeline. Overall, when compared to a recent open-source GPU solution implemented on a modern 8nm process node, our accelerator implemented on an older 20nm FPGA achieves an average speedup of 9.7times. Although our accelerator is 7.9times slower than most examples of a state-of-the-art CPU solution implemented on a recent 7nm process node, we describe future extensions that can make FPGA acceleration provide attractive Pareto-optimal tradeoffs.",
• keywords = "genetic algorithms, genetic programming, Embedded systems, Shape, Pipelines, Graphics processing units, Machine learning, Parallel processing, reconfigurable computing, FPGA devices",
• DOI = "doi:10.1109/CASES55004.2022.00015",
• ISSN = "2643-1726",
• month = oct,
• notes = "Also known as \cite{9933164}",

}

Genetic Programming entries for Christopher C Crary Wesley Piard Britton Chesley Greg Stitt

Citations