Exploring embedded-systems architectures with Artemis

TLDR

Embedded systems must balance low cost, power efficiency, and high programmability, leading to heterogeneous multiprocessor architectures that complicate performance prediction and require early design tools. Artemis aims to efficiently explore the design space of heterogeneous embedded‑systems architectures at multiple abstraction levels for a wide range of applications. It offers a modeling and simulation framework that evaluates architectural trade‑offs across multiple abstraction levels for diverse applications. Two studies using Artemis yielded promising results, providing valuable feedback on architectural decisions for the evaluated applications.

Abstract

Because embedded systems mostly target mass production and often run on batteries, they should be cheap to realize and power efficient. In addition, they require a high degree of programmability to provide real-time performance for multiple applications and standards. However, performance requirements as well as cost and power-consumption constraints demand that substantial parts of these systems be implemented in dedicated hardware blocks. As a result, their heterogeneous system architecture consists of components ranging from fully dedicated hardware components for time-critical application tasks. Increasingly, these designs yield heterogeneous embedded multiprocessor systems that reside together on a single chip. The heterogeneity of these highly programmable systems and the varying demands of their target applications greatly complicate system design. The increasing complexity of embedded-system architectures makes predicting performance behavior more difficult. Therefore, having the appropriate tools to explore different choices at an early design stage is increasingly important. The Artemis modeling and simulation environment aims to efficiently explore the design space of heterogeneous embedded-systems architectures at multiple abstraction levels and for a wide range of applications targeting these architectures. The authors describe their of this methodology in two studies that showed promising results, providing useful feedback on a wide range of design decisions involving the architectures for the two applications.