Memory Analysis and Optimized Allocation of Dataflow Applications on Shared-Memory MPSoCs

Abstract

The majority of applications, ranging from the low complexity to very multifaceted entities requiring dedicated hardware accelerators, are very well suited for Multiprocessor Systems-on-Chips (MPSoCs). It is critical to understand the general characteristics of a given embedded application: its behavior and its requirements in terms of MPSoC resources. This paper presents a complete method to study the important aspect of memory characteristic of an application. This method spans the theoretical, architecture-independent memory characterization to the quasi optimal static memory allocation of an application on a real shared-memory MPSoCs. The application is modeled as an Synchronous Dataflow (SDF) graph which is used to derive a Memory Exclusion Graph (MEG) essential for the analysis and allocation techniques. Practical considerations, such as cache coherence and memory broadcasting, are extensively treated. Memory footprint optimization is demonstrated using the example of a stereo matching algorithm from the computer vision domain. Experimental results show a reduction of the memory footprint by up to 43 % compared to a state-of-the-art minimization technique, a throughput improvement of 33 % over dynamic allocation, and the introduction of a tradeoff between multicore scheduling flexibility and memory footprint.