Representation of Evolutionary Algorithms in FPGA Cluster for Project of Large-Scale Networks

TL;DR

This paper explores implementing evolutionary algorithms on FPGA clusters to efficiently solve large-scale network problems, expanding from single FPGA to multi-FPGA systems for better scalability.

Contribution

It presents a method to partition and implement Node-Depth Encoding based evolutionary algorithms on multi-FPGA clusters, enabling larger network problem sizes.

Findings

Successful expansion from 512 to 4096 nodes

Improved performance with multi-FPGA implementation

Effective representation of large-scale network problems

Abstract

Many problems are related to network projects, such as electric distribution, telecommunication and others. Most of them can be represented by graphs, which manipulate thousands or millions of nodes, becoming almost an impossible task to obtain real-time solutions. Many efficient solutions use Evolutionary Algorithms (EA), where researches show that performance of EAs can be substantially raised by using an appropriate representation, such as the Node-Depth Encoding (NDE). The objective of this work was to partition an implementation on single-FPGA (Field-Programmable Gate Array) based on NDE from 512 nodes to a multi-FPGAs approach, expanding the system to 4096 nodes.