On-Chip Embedded Instruments Data Fusion and Life-Time Prognostics of Dependable VLSI-SoCs using Machine-Learning

Nowadays, a rapid introduction of very complex nanometer Many-Processor Systems-on-Chip in safety-critical applications is taking place. Unfortunately, it pairs with an unacceptable decrease in dependability of these complex nanosystems if no additional countermeasures are taken. To address this challenge, a promising approach is presented in this paper that uses a set of IJTAG compatible embedded instruments (EIs), in and around a processor cores to monitor their present health status. Data from these EIs is collected and fused for lifetime prognostics and hence dependability. For the EIs data fusion, use is made of principal component analysis (PCA) technique. For lifetime prediction based on different EIs, power-law degradation model was used.