Spectral techniques for high-resolution thermal characterization with limited sensor data

Abstract

Elevated chip temperatures are true limiters to the scalability of computing systems. Excessive runtime thermal variations com-promise the performance and reliability of integrated circuits. To address these thermal issues, state-of-the-art chips have integrated thermal sensors that monitor temperatures at a few selected die lo-cations. These temperature measurements are then used by ther-mal management techniques to appropriately manage chip perfor-mance. Thermal sensors and their support circuitry incur design overheads, die area, and manufacturing costs. In this paper, we pro-pose a new direction for full thermal characterization of integrated circuits based on spectral Fourier analysis techniques. Application of these techniques to temperature sensing is based on the observa-tion that die temperature is simply a space-varying signal, and that space-varying signals are treated identically to time-varying signals in signal analysis. We utilize Nyquist-Shannon sampling theory to devise methods that can almost fully reconstruct the thermal status of an integrated circuit during runtime using a minimal number of thermal sensors. We propose methods that can handle uniform and non-uniform thermal sensor placements. We develop an extensive experimental setup and demonstrate the effectiveness of our meth-ods by thermally characterizing a 16-core processor. Our method produces full thermal characterization with an average absolute er-ror of 0.6 % using a limited number of sensors