Neu Pow: Artificial Neural Networks for Power and Behavioral Modeling of Arithmetic Components in 45nm ASICs Technology

Sau C.;Fanni T.;Palumbo F.;Raffo L.
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×.
2019
Inglese
ACM International Conference on Computing Frontiers 2019, CF 2019 - Proceedings
9781450366854
Association for Computing Machinery, Inc
183
189
7
http://dl.acm.org/citation.cfm?id=3310273
16th ACM International Conference on Computing Frontiers, CF 2019
Contributo
Comitato scientifico
2019
Alghero (Italia)
internazionale
scientifica
45nm Technology; ANNs; Arithmetic components; ASICs; Characterization; Estimation; Low power; Modeling; Power consumption
4 Contributo in Atti di Convegno (Proceeding)::4.1 Contributo in Atti di convegno
Nasser, Y.; Sau, C.; Prevotet, J. -C.; Fanni, T.; Palumbo, F.; Helard, M.; Raffo, L.
273
7
4.1 Contributo in Atti di convegno
none
info:eu-repo/semantics/conferencePaper
Files in This Item:
There are no files associated with this item.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Questionnaire and social

Share on:
Impostazioni cookie