Methodologies for Design Space Exploration

Pimentel, Andy D.

doi:10.1007/978-981-15-6401-7_23-1

Cited by 3 publications

(1 citation statement)

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“…An overview of DSE and its concepts can be found in [18]. State-of-the-art DSE approaches can be divided into exact and non-exact (meta-heuristic) design space searching methods [19]. Non-exact methods often do not explore the design space systematically, potentially leading to a duplicate evaluation of individual design points and degraded runtime performance [20].…”

Section: Related Workmentioning

confidence: 99%

Generative Design of the Architecture Platform in Multiprocessor System Design

Müller,

Schumacher,

Steffen

et al. 2024

Electronics

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When designing a system at the Electronic System Level (ESL), designers are confronted with a very large number of design decisions, each affecting the characteristics of the resulting system. Simultaneously, the demands for the system’s performance, reliability, and energy consumption have increased drastically. Design Space Exploration (DSE) aims to facilitate this complex task by automating the system synthesis and traversing the design space autonomously. Previous studies on DSE have mainly considered fixed architectures with a fixed set of hardware components only. In the paper at hand, we overcome this limitation to allow for a higher degree of freedom in the design of a multiprocessor system. Instead of a fixed architecture as input, we are using a resource library containing resource types whose instances can then be arbitrarily placed and connected. More specifically, we enable the exploration of the types, the number, and the positions of required processing-type instances in a grid-based topology template in addition to deciding on the remaining system synthesis tasks, namely, resource allocation, task binding, routing, and scheduling. We provide an extensible framework, based on Answer Set Programming (ASP) modulo Theories (ASPmT), for generating system architectures fulfilling predefined constraints. Our studies show that this higher degree of freedom, originating from fewer restrictions regarding the architecture, leads to an increased complexity of the problem. In extensive experiments, we show scalability trends for a set of parameters, demonstrating the capabilities and limits of our approach.

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Section: Related Workmentioning

confidence: 99%