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All mobile applications require high performances with very long battery life. The speed and power consumption trade-off clearly appears as a prominent challenge to optimize the overall energy efficiency. In Multiprocessor System-On-Chip architectures, the trade-off is usually achieved by dynamically adapting the supply voltage and the operating frequency of a processor cluster or of each processor at fine grain. This requires monitoring accurately, on-chip and at runtime, the supply voltage and temperature across the die. Within this context, this paper introduces a method to es timate, from on-chip measurements, using embedded statistical tests, the supply voltage and temperature of small die area using low-cost digital sensors featuring a set of ring oscillators solely. The results obtained, considering a 32nm process, demonstrate the efficiency of the proposed method. Indeed, voltage and tempera ture measurement errors are kept, in average, below 5mV and 7°C, respectively. [ABSTRACT FROM AUTHOR]

All mobile applications require high performances with very long battery life. The speed and power consumption trade-off clearly appears as a prominent challenge to optimize the overall energy efficiency. In MultiProcessor System-On-Chip architectures, the trade-off is usually achieved by dynamically adapting the supply voltage and the operating frequency of a processor cluster or of each processor at fine grain. This requires monitoring accurately, on-chip and at runtime, the supply voltage and temperature across the die. Within this context, this paper introduces a method to estimate, from on-chip measurements, using embedded statistical tests, the supply voltage and temperature of small die area using low-cost digital sensors featuring a set of ring oscillators solely. The results obtained, considering a 32nm process, demonstrate the efficiency of the proposed method. Indeed, voltage and temperature measurement errors are kept, in average, below 5mV and 7°C, respectively. [ABSTRACT FROM PUBLISHER]