Maximizing hotspot temperature: Wavelet based modelling of heating and cooling profile of functional workloads

Localized heating leads to generation of thermal hotspots that affect performance and reliability of a chip. Functional workloads determine the locations and temperature of hotspots on a die. Programs are classified into phases based on program execution profile. During a phase, spatial power dissipation pattern of an application remains unchanged. In this paper we present a systematic approach for developing a synthetic workload which is formed by a combination of phases extracted from functional workload which maximizes the temperature of a hotspot. Hotspot temperature is determined not only by the current activity in that region, but also by the past activities in the surrounding regions. Therefore, if the surrounding areas were “pre-heated” with a different workload, then the target region may become hotter due to slower rate of lateral heat dissipation. In this paper a wavelet-based canonical power dissipation model is developed to capture the temporal and spatial behavior of the power traces. This is followed by an Integer Linear Programming approach which is used to determine the sequence of these program phases in order to create a worst case temperature at the Hotspot. The novel contributions of this paper are (i) wavelet based technique to model spatio-temporal power variation for the phases in the functional workload and a (ii) linear programming scheme that arranges program phases to create the worst case temperature.