The impact of process variations and wear-out mechanisms in current and next generation technology nodes is becoming relevant and cannot be compensated at the device or architectural level. Intra-die process variations raising at the core level and platform level makes parallel multicore platforms intrinsically heterogeneous, because the various cores are clocked at different operational frequencies. Power consumption becomes heterogeneous too, both considering dynamic and leakage consumption. Wear-out processes add further uncertainty over time. In this context, to fully exploit the computational capability of the platform parallelism, variability and wear-out aware task allocation strategies must be developed. In this work, we discuss techniques to perform task allocation and we show how they can be integrated in a software toolchain. We report results of the implementation of variability and wear-out awareness in state-of-art multicore platforms.

Toolchain integration of runtime variability and aging awareness in multicore platforms / Nittala, VENKATA RAMAKRISHNA; Barchi, Francesco; Urgese, Gianvito; Acquaviva, Andrea. - ELETTRONICO. - (2016). (Intervento presentato al convegno Forum on specification & Design Languages FDL 2016 tenutosi a Bremen, Germany nel 14-16 September 2016) [10.1109/FDL.2016.7880384].

Toolchain integration of runtime variability and aging awareness in multicore platforms

NITTALA, VENKATA RAMAKRISHNA;BARCHI, FRANCESCO;URGESE, GIANVITO;ACQUAVIVA, ANDREA
2016

Abstract

The impact of process variations and wear-out mechanisms in current and next generation technology nodes is becoming relevant and cannot be compensated at the device or architectural level. Intra-die process variations raising at the core level and platform level makes parallel multicore platforms intrinsically heterogeneous, because the various cores are clocked at different operational frequencies. Power consumption becomes heterogeneous too, both considering dynamic and leakage consumption. Wear-out processes add further uncertainty over time. In this context, to fully exploit the computational capability of the platform parallelism, variability and wear-out aware task allocation strategies must be developed. In this work, we discuss techniques to perform task allocation and we show how they can be integrated in a software toolchain. We report results of the implementation of variability and wear-out awareness in state-of-art multicore platforms.
2016
979-10-92279-17-7
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2687167