An OpenMP Parallel Genetic Algorithm for Design Space Exploration of Heterogeneous Multi-processor Embedded Systems

Abstract

Heterogeneous multiprocessor platforms are becoming widespread in the embedded system domain, mainly for the opportunity to improve timing performance and to minimize energy/power consumption and costs. Therefore, when using such platforms, it is important to adopt a Design Space Exploration (DSE) strategy that considers compromises among different objectives. Existing DSE approaches are generally based on evolutionary algorithms to solve Multi-Objective Optimization Problems (MOOPs) by minimizing a linear combination of weighted cost functions (i.e., Weighted Sum Method, WSM). In this way, the main issues are related to reduce timing execution while trying to improve the evolutionary algorithm performance, introducing strategies that attempt to bring better solutions. Code parallelization is one of the most used approaches in this field, but no standard methods have been released since different aspects could affect the performance. This approach leads to exploit parallel and distributed processing elements in order to implement evolutionary algorithms. In the latter case, if we consider genetic algorithms, it is possible to talk about Parallel Genetic Algorithms (PGA). Considering this context, this paper focuses on DSE for heterogeneous multi-processor embedded systems and introduces an improvement that reduces execution time using parallel programming languages (i.e., OpenMP) inside the main genetic algorithm approach, while trying to lead to better partitioning solutions. The descriptions of the adopted DSE activities and the OpenMP implementation, validated by means of a case study, represent the core of the paper. CCS CONCEPTS • Computer systems organization → Embedded systems; Embedded hardware;

ArchGym: An Open-Source Gymnasium for Machine Learning Assisted Architecture Design

“…RL has been used for DNN architecture design [51], chip floor planning [70], and exploring router-less NoC designs [63]. GA has been used in design space exploration of heterogeneous multiprocessor embedded systems [73] and behavioral IPs [87]. ACO has been applied to solving long-standing compiler optimization problems [93] and has been used in high-level synthesis design space exploration [30].…”

Distributed Approach for the Indoor Deployment of Wireless Connected Objects by the Hybridization of the Voronoi Diagram and the Genetic Algorithm

“…Figure 1: Different types of parallel Genetic Algorithms [57] The objective of this study is to design a distributed approach where the hybridization of the Voronoi diagram and the GA is executed in parallel in several nodes. The VD is essentially used to divide the region of interest into cells and generate initial solutions that present the initial population of the GA. Then, this population will be divided into in sub-populations which will be, later, distributed to the different nodes constituting the employed data collection network.…”

Untitled

“…In [20], they implemented their solution on a multi-core system using MATLAB. The PGA is also implemented for Design Space Exploration using OpenMP, as in [21]. Other researches present how to speed up GA using OpenMP on multicore processors [22,23].…”