Composable thermal modeling and simulation for architecture-level thermal designs of multicore microprocessors

Abstract

Efficient temperature estimation is vital for designing thermally efficient, lower power and robust integrated circuits in nanometer regime. Thermal simulation based on the detailed thermal structures no longer meets the demanding tasks for efficient design space exploration. The compact and composable model-based simulation provides a viable solution to this difficult problem. However, building such thermal models from detailed thermal structures was not well addressed in the past. In this article, we propose a new compact thermal modeling technique, called ThermComp, standing for thermal modeling with composable modules. ThermComp can be used for fast thermal design space exploration for multicore microprocessors. The new approach builds the composable model from detailed structures for each basic module using the finite difference method and reduces the model complexity by the sampling-based model order reduction technique. These composable models are then used to assemble different multicore architecture thermal models and realized into SPICE-like netlists. The resulting thermal models can be simulated by the general circuit simulator SPICE. ThermComp tries to preserve the accuracy of fine-grained models with the speed of coarse-grained models. Experimental results on a number of multicore microprocessor architectures show the new approach can easily build accurate thermal systems from compact composable models for fast architecture thermal analysis and optimization and is much faster than the existing HotSpot method with similar accuracy.

Probabilistic Analysis of Steady-State Temperature and Maximum Frequency of Multicore Processors considering Workload Variation

“…To explore the design space of thermalaware multicore processors at the early stage, some thermal models have been proposed to estimate the temperature and performance of processors [2][3][4][5], and most of estimation approaches are based on transient analysis [6][7][8][9][10][11][12][13][14]. For transient analysis, temporal variations of temperature and performance depending on workloads are traced, contributing to high estimation accuracy.…”

“…To improve the accuracy of thermal simulation, Jang et al [11] made an extension to the thermal model for HotSpot by taking into account the different ambient temperature owing to workload variations. To accelerate thermal analysis of multicore processors at the architecture level, Wang et al [5] presented a composite thermal model, termed Therm-Comp, to optimize the model for different large processors. Li et al [4] proposed a parameterized architecture-level dynamic thermal model, namely, ParThermPOF, in which many parameters can be set such as the location of thermal sensors and the conductivity of different components.…”

Improving the accuracy of module based thermal modeling method for VLSI circuits design

“…The module based thermal modeling method [4], [5] achieves the accuracy and analysis efficiency at the same time by dividing the chip into basic building blocks called modules. The module model is reduced by model order reduction (MOR) and assembled according to floorplan to form the model of the whole chip.…”