In this paper, we present a flexible, simple and accurate power modeling technique that can be used to estimate the power consumption of modern technology devices. We exploit Artificial Neural Networks for power and behavioral estimation in Application Specific Integrated Circuits. Our method, called NeuPow, relies on propagating the predictors between the connected neural models to estimate the dynamic power consumption of the individual components. As a first proof of concept, to study the effectiveness of NeuPow, we run both component level and system level tests on the Open GPDK 45 nm technology from Cadence, achieving errors below 1.5% and 9% respectively for component and system level. In addition, NeuPow demonstrated a speed up factor of 2490×.
Neu Pow: Artificial Neural Networks for Power and Behavioral Modeling of Arithmetic Components in 45nm ASICs Technology / Nasser, Y., Sau, C., Prevotet, J.-C., Fanni, T., Palumbo, F., Helard, M., Raffo, L.. - (2019), pp. 183-189. (16th ACM International Conference on Computing Frontiers, CF 2019 ita 2019) [10.1145/3310273.3322820].
Neu Pow: Artificial Neural Networks for Power and Behavioral Modeling of Arithmetic Components in 45nm ASICs Technology
Palumbo F.;
2019-01-01
Abstract
In this paper, we present a flexible, simple and accurate power modeling technique that can be used to estimate the power consumption of modern technology devices. We exploit Artificial Neural Networks for power and behavioral estimation in Application Specific Integrated Circuits. Our method, called NeuPow, relies on propagating the predictors between the connected neural models to estimate the dynamic power consumption of the individual components. As a first proof of concept, to study the effectiveness of NeuPow, we run both component level and system level tests on the Open GPDK 45 nm technology from Cadence, achieving errors below 1.5% and 9% respectively for component and system level. In addition, NeuPow demonstrated a speed up factor of 2490×.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
