University of Florence
In this paper, the author's present novel 2D geospatial-based predictive models for exploring the complex thermal spatial behavior of Three-Dimensional (3D) die stacked multi-core processors at the early design stage. Unlike other analytical techniques, their predictive models can forecast the location, size and temperature of thermal hotspots. They evaluate the efficiency of using the models for predicting within-die and cross-dies thermal spatial characteristics of 3D multi-core architectures with widely varied design choices. Their results show the models achieve high accuracy while maintaining low complexity and computation overhead.