Invasive Tightly-Coupled Processor Arrays

Abstract

We introduce a novel class of massively parallel processor architectures called invasive Tightly-Coupled Processor Arrays (TCPAs). The presented processor class is a highly parameterizable template which can be tailored before runtime to fulfill costumers' requirements such as performance, area cost, and energy efficiency. These programmable accelerators are well suited for domain-specific computing from the areas of signal, image, and video processing as well as other streaming processing applications. To overcome future scaling issues (e.g., power consumption, reliability, resource management, as well as application parallelization and mapping), TCPAs are inherently designed in way that they support self-adaptivity and resource awareness at hardware level. Here, we follow a recently introduced resource-aware parallel computing paradigm called invasive computing where an application can dynamically claim, execute, and release the resources. Furthermore, we show how invasive computing can be used as an enabler for power management. For the first time, we present a seamless mapping flow for TCPAs, based on a domain-specific language. Moreover, we outline a complete symbolic mapping approach. Finally, we support our claims by comparing a TCPA against an ARM Mali-T604 GPU in terms of performance and energy efficiency.