A Method for Run-Time Prediction of On-Chip Thermal Conditions in Dynamically Reconfigurable SOPCs

AbstractAutonomous mobile systems nowadays deploy FPGA-based System on Programmable Chips (SoPCs) for supporting their dynamic multitask multimodal workloads. For such field-deployed systems, activation times, execution periods of tasks, and variations in environmental conditions are usually difficult to predict. These dynamic variations result in a new challenge of dynamic thermal cycling stress on the SoPC die, which can result in transient and even permanent hardware faults in the computing system. This paper proposes the approach of run-time structural adaptation (RTSA) to mitigate dynamic thermal cycling stress on the SoPC dies. RTSA assumes the tasks to have multiple implementation variants, called Application Specific Processing (ASP) circuit variants, which vary in hardware resources, operating frequency, and power consumption. Dynamically reconfiguring appropriate ASP circuit variants of tasks allow systems to maintain their die temperature in the desired range while taking into account variations in power budget and modes of operation. This means the essence of RTSA is a decision-making mechanism which can select at run-time, a suitable system configuration (set of ASP circuit variants of active tasks), whenever needed, to meet the die temperature constraints. To do so, run-time die temperature prediction for potential system configurations using an estimation model is required. This paper presents a generic method to derive an analytical model for any SoPC that can estimate the die temperature in real time and thus support the decision-making mechanism. To develop this method, the thermal behavior of SoPC die under different task scenarios is studied and relation of die temperature to frequency, resource utilization, and power consumption is analyzed. An RTSA-enabled experimental platform is set up on Xilinx Zynq XC7Z020 SoPC for this purpose. Experimental results also demonstrate that the proposed method can be used to derive a model in run-time, thus enabling systems to self-derive and dynamically update the model in run-time.