Abstract
One of the growing areas of application of embedded systems in robotics, aerospace, military, etc. is autonomous mobile systems. Usually, such embedded systems have multitask multimodal workloads. These systems must sustain the required performance of their dynamic workloads in presence of varying power budget due to rechargeable power sources, varying die temperature due to varying workloads and/or external temperature, and varying hardware resources due to occurrence of hardware faults. This paper proposes a run-time decision-making method, called Decision Space Explorer, for FPGA-based Systems-on-Chip (SoCs) to support changing workload requirements while simultaneously mitigating unpredictable variations in power budget, die temperature, and hardware resource constraints. It is based on the concept of Run-Time Structural Adaptation (RTSA); whenever there is a change in a system’s set of constraints, Explorer selects a suitable hardware processing circuit for each active task at an appropriate operating frequency such that all the constraints are satisfied. Explorer has been experimentally deployed on the ARM Cortex-A9 core of Xilinx Zynq XC7Z020 SoC. Its worst-case decision-making time for different scenarios ranges from tens to hundreds of microseconds. Explorer is thus suitable for enabling RTSA in systems where specifications of multiple objectives must be maintained simultaneously, making them self-sustainable.
Highlights
As the famous proverb goes, “necessity is the mother of invention,” considering the aspect of “necessity,” over the past few decades, human necessity has significantly increased in every walk of life. ere is a necessity to delegate much of the human workload to autonomous and mobile systems, from routine chores to industrial robotics, to aerospace, to defense, and to many other areas where presence of humans is not efficient, or is unsafe or even dangerous for humans
It is understood from the above observation of the literature and the Introduction section that Run-Time Structural Adaptation (RTSA) seems to be the potential solution for Field Programmable Gate Arrays (FPGAs)-based systems to be selfsustainable against (a) multitask and multimodal workloads with unpredictable combination of tasks activated for parallel execution, (b) unpredictable variations of external to SoC and environmental factors, and (c) unpredictable variation of hardware resource constraints caused by transient or permanent hardware faults
Us, the process of RTSA becomes seamless when the static Multimode Adaptive Collaborative Reconfigurable self-Organized System” (MACROS) framework is deployed on the FPGA, and the ASP circuit variants of all the tasks are appropriately packed into Collaborative Macro-Functional Units (CMFUs)/partial bitstreams at design time
Summary
As the famous proverb goes, “necessity is the mother of invention,” considering the aspect of “necessity,” over the past few decades, human necessity has significantly increased in every walk of life. ere is a necessity to delegate much of the human workload to autonomous and mobile systems, from routine chores to industrial robotics, to aerospace, to defense, and to many other areas where presence of humans is not efficient, or is unsafe or even dangerous for humans. Us, the challenge faced by autonomous and mobile systems is that they must be capable of sustaining the performance requirements of their dynamic multitask multimodal workload while simultaneously adapting to the dynamic changes in the power budget, die temperature, and hardware resource conditions. Us, there is a need to devise a method, which can drastically reduce the set of variants on the decision space reducing the exploration time and making it acceptable for run-time adaptation Such a method should be able to select the best system configuration that satisfies the multiple constraints whenever there is a change in the set of requirements. Erefore, it allows structural adaptation to dynamic changes in multiple interrelated constraints, in run-time, for FPGA-based multitask multimodal systems, making them self-sustainable!.
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