ANALYSIS AND TOOLS FOR THE DESIGN OF VLIW EMBEDDED SYSTEMS IN A MULTI-OBJECTIVE SCENARIO

2007 ◽  
Vol 16 (05) ◽  
pp. 819-846
Author(s):  
VINCENZO CATANIA ◽  
MAURIZIO PALESI ◽  
DAVIDE PATTI
Keyword(s):  
Embedded Systems ◽  
Instruction Scheduling ◽  
Design Flow ◽  
System Level ◽  
Instruction Level Parallelism ◽  
Accurate Estimation ◽  
Subsequent Stage ◽  
Multi Objective ◽  
Trade Offs ◽  
Starting Point

The use of Application-Specific Instruction-set Processors (ASIP) in embedded systems is a solution to the problem of increasing complexity in the functions these systems have to implement. Architectures based on Very Long Instruction Word (VLIW) have found fertile ground in multimedia electronic appliances thanks to their ability to exploit high degrees of Instruction Level Parallelism (ILP) with a reasonable trade-off in complexity and silicon costs. In this case the ASIP specialization involves a complex interaction between hardware- and software-related issues. In this paper we propose tools and methodologies to cope efficiently with this complexity from a multi-objective perspective. We present EPIC-Explorer, an open platform for estimation and system-level exploration of an EPIC/VLIW architecture. We first analyze the possible design objectives, showing that it is necessary, given the fundamental role played by the VLIW compiler in instruction scheduling, to evaluate the appropriateness of ILP-oriented compilation on a case-by-case basis. Then, in the architecture exploration phase, we will use a multi-objective genetic approach to obtain a set of Pareto-optimal configurations. Finally, by clustering the configurations thus obtained, we extract those representing possible trade-offs between the objectives, which are used as a starting point for evaluation via more accurate estimation models at a subsequent stage in the design flow.

scholarly journals A Case for Security-Aware Design-Space Exploration of Embedded Systems

2020 ◽  
Vol 10 (3) ◽  
pp. 22
Author(s):  
Andy D. Pimentel
Keyword(s):  
Embedded Systems ◽  
Embedded System ◽  
Design Space Exploration ◽  
Design Space ◽  
Space Exploration ◽  
Systems Design ◽  
Design System ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Trade Offs

As modern embedded systems are becoming more and more ubiquitous and interconnected, they attract a world-wide attention of attackers and the security aspect is more important than ever during the design of those systems. Moreover, given the ever-increasing complexity of the applications that run on these systems, it becomes increasingly difficult to meet all security criteria. While extra-functional design objectives such as performance and power/energy consumption are typically taken into account already during the very early stages of embedded systems design, system security is still mostly considered as an afterthought. That is, security is usually not regarded in the process of (early) design-space exploration of embedded systems, which is the critical process of multi-objective optimization that aims at optimizing the extra-functional behavior of a design. This position paper argues for the development of techniques for quantifying the ’degree of secureness’ of embedded system design instances such that these can be incorporated in a multi-objective optimization process. Such technology would allow for the optimization of security aspects of embedded systems during the earliest design phases as well as for studying the trade-offs between security and the other design objectives such as performance, power consumption and cost.

scholarly journals A Comparison of Genetic Representations and Initialisation Methods for the Multi-objective Shortest Path Problem on Multigraphs

2021 ◽  
Vol 2 (3) ◽  
Author(s):  
Lilla Beke ◽  
Michal Weiszer ◽  
Jun Chen
Keyword(s):  
Shortest Path ◽  
Time Budget ◽  
Exact Algorithm ◽  
Simple Graph ◽  
Shortest Path Problem ◽  
Future Application ◽  
Multi Objective ◽  
Trade Offs ◽  
Conflicting Objectives ◽  
Problem Instances

AbstractThis paper compares different solution approaches for the multi-objective shortest path problem (MSPP) on multigraphs. Multigraphs as a modelling tool are able to capture different available trade-offs between objectives for a given section of a route. For this reason, they are increasingly popular in modelling transportation problems with multiple conflicting objectives (e.g., travel time and fuel consumption), such as time-dependent vehicle routing, multi-modal transportation planning, energy-efficient driving, and airport operations. The multigraph MSPP is more complex than the NP-hard simple graph MSPP. Therefore, approximate solution methods are often needed to find a good approximation of the true Pareto front in a given time budget. Evolutionary algorithms have been successfully applied for the simple graph MSPP. However, there has been limited investigation of their applications to the multigraph MSPP. Here, we extend the most popular genetic representations to the multigraph case and compare the achieved solution qualities. Two heuristic initialisation methods are also considered to improve the convergence properties of the algorithms. The comparison is based on a diverse set of problem instances, including both bi-objective and triple objective problems. We found that the metaheuristic approach with heuristic initialisation provides good solutions in shorter running times compared to an exact algorithm. The representations were all found to be competitive. The results are encouraging for future application to the time-constrained multigraph MSPP.

scholarly journals Multi-Objective Optimization of Autonomous Microgrids with Reliability Consideration

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4466
Author(s):  
Maël Riou ◽  
Florian Dupriez-Robin ◽  
Dominique Grondin ◽  
Christophe Le Loup ◽  
Michel Benne ◽  
...  
Keyword(s):  
Power Systems ◽  
Rural Areas ◽  
Energy System ◽  
Design Stage ◽  
Multi Objective Optimization ◽  
Renewable Energy Resources ◽  
Nsga Ii ◽  
Renewable Integration ◽  
Multi Objective ◽  
Trade Offs

Microgrids operating on renewable energy resources have potential for powering rural areas located far from existing grid infrastructures. These small power systems typically host a hybrid energy system of diverse architecture and size. An effective integration of renewable energies resources requires careful design. Sizing methodologies often lack the consideration for reliability and this aspect is limited to power adequacy. There exists an inherent trade-off between renewable integration, cost, and reliability. To bridge this gap, a sizing methodology has been developed to perform multi-objective optimization, considering the three design objectives mentioned above. This method is based on the non-dominated sorting genetic algorithm (NSGA-II) that returns the set of optimal solutions under all objectives. This method aims to identify the trade-offs between renewable integration, reliability, and cost allowing to choose the adequate architecture and sizing accordingly. As a case study, we consider an autonomous microgrid, currently being installed in a rural area in Mali. The results show that increasing system reliability can be done at the least cost if carried out in the initial design stage.

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