Harnessing Adaptivity Analysis for the Automatic Design of Efficient Embedded and HPC Systems

Abstract

In the past decades, design methodologies of Embedded Systems (ES) and High Performance Computing (HPC) systems have evolved following different trends. However, they are lately experiencing issues that affect both the domains, whose solutions converge to similar approaches. Examples of issues affecting both the domains are: large parallelism degrees, heterogeneity, power constraints, reliability issues, self-adaptation, and significant programming efforts to reach the desired performance on increasingly complex architectures. Systems able to dynamically adjust their behavior at run-time appear good candidates for the next computing generation, and will most probably condemn non-adaptable systems to rapid extinction. Adaptive systems can deal with uncertain and unpredictable conditions, due, for example, to reliability issues. In this paper we show how we can exploit adaptivity analysis to address several design challenges in embedded systems. The results show an average increase in performance around 34% with respect to state of the art methodology, with a limited area overhead. Furthermore, we discuss our work-in-progress on the exploitation of adaptivity analysis to address new challenges in HPC systems design.