With ever-increasing power densities, Dynamic Thermal Management (DTM) techniques have become mainstream in today's systems. An important component of such techniques is the thermal trigger. It has been shown that predictive thermal triggers can outperform reactive ones [4]. In this paper, we present a novel trade-off space of predictive thermal triggers, and compare different approaches proposed in the literature. We argue that run-time adaptability is a crucial parameter of interest. We present a run-time adaptable thermal simulator compatible with arbitrary sensor configuration based on the Neural Network (NN) simulator presented in [14]. We present experimental results on Niagara UltraSPARC T1 chip with real-life benchmark applications. Our results quantitatively establish the effectiveness of the proposed simulator for reducing (by up to 90%), the otherwise unacceptably high errors, that can arise due to expected leakage current variation and design-time thermal modeling errors.
['Pratyush Kumar', 'David Atienza']
Run-time adaptable on-chip thermal triggers