In the context of coarse-grained reconfigurable systems we present a power estimation model to guide the designer in deciding which part of the design may benefit from the application of a power gating technique. The model is assessed by adopting a reconfigurable core for image processing targeting an ASIC 90 nm technology.

1. POWER MODELLING FOR
SAVING STRATEGIES IN
COARSE GRAINED
RECONFIGURABLE SYSTEMS
Francesca Palumbo
University of Sassari
PolComIng
Information Engineering Unit
Tiziana Fanni, Carlo Sau, Paolo Meloni
and Luigi Raffo
University of Cagliari
Dept. of Electrical and Electronics Eng.
tiziana.fanni@unica.it.
2015 International Conference on
ReConFigurable Computing and FPGAs
E
O
L
A
B

2. OUTLINE
 Introduction
 Coarse-Graine Reconfiguration
 Multi Dataflow Composer tool
 Static Power Estimation Model
 PHD Plan
 Improve power estimation model
 Assess the technology impact
 Automate the strategy
ReConFig2015-Dec7-92015,MayanRiviera(Mexico)–TizianaFanni
2

3. INTRODUCTION
Coarse-Graine Reconfiguration
ReConFig2015-Dec7-92015,MayanRiviera(Mexico)–TizianaFanni
3
A
in1
D
in2
F
in3
E
G
B
Sbox_1
Sbox_2
out1
C
Sbox_0
ID net=1
LUTCoarse Grained Reconfigurable Platform
A B C
D E C
A
F
CG
Dataflow Descriptions
N:1
α
β
γ
α
β
γ
0 0
0

4. INTRODUCTION
Coarse-Graine Reconfiguration



ReConFig2015-Dec7-92015,MayanRiviera(Mexico)–TizianaFanni
4
A
in1
D
in2
F
in3
E
G
B
Sbox_1
Sbox_2
out1
C
Sbox_0
ID net=3
LUTCoarse Grained Reconfigurable Platform
A B C
D E C
A
F
CG
Dataflow Descriptions
N:1
α
β
γ
α
β
γ
1 1
0

5. INTRODUCTION
Coarse-Graine Reconfiguration
Power consumption  Main Power Saving Methodologies
 Dynamic:  Clock-gating
 Static: Power-gating
ReConFig2015-Dec7-92015,MayanRiviera(Mexico)–TizianaFanni
4
A
in1
D
in2
F
in3
E
G
B
Sbox_1
Sbox_2
out1
C
Sbox_0
ID net=3
LUTCoarse Grained Reconfigurable Platform
A B C
D E C
A
F
CG
Dataflow Descriptions
N:1
α
β
γ
α
β
γ
1 1
0

6. INTRODUCTION
http://sites.unica.it/rpct/
5
Multi-Dataflow Composer Tool
ReConFig2015-Dec7-92015,MayanRiviera(Mexico)–TizianaFanni
A
in1
D
in2
F
in3
E
G
B
Sbox_1
Sbox_2
out1
C
LR1
LR2: Always ON LR
LR3
LR4
LR5
LR4
RTN
RTN
RTN
RTN
RTN
RTN
ISOL
ISOL
ISOLISOL
Sbox_0
ISOL
VDD
SW
VDD
SW
VDD
SW
ISOL
VDD
SW
ID net
Power
Controller
CG_en1
CG_en4
CG_en5
CG_en3
clock

7. INTRODUCTION
http://sites.unica.it/rpct/
5
Multi-Dataflow Composer Tool
ReConFig2015-Dec7-92015,MayanRiviera(Mexico)–TizianaFanni
A
in1
D
in2
F
in3
E
G
B
Sbox_1
Sbox_2
out1
C
LR1
LR2: Always ON LR
LR3
LR4
LR5
LR4
RTN
RTN
RTN
RTN
RTN
RTN
ISOL
ISOL
ISOLISOL
Sbox_0
ISOL
VDD
SW
VDD
SW
VDD
SW
ISOL
VDD
SW
ID net
Power
Controller
CG_en1
CG_en4
CG_en5
CG_en3
clock
PG huge resources overhead

8. STATIC POWER ESTIMATION MODEL
 Parameters determining the power consumption:
 architectural parameters
 functional parameters
 technological parameters

ReConFig2015-Dec7-92015,MayanRiviera(Mexico)–TizianaFanni
6

9. STATIC POWER ESTIMATION MODEL
 Parameters determining the power consumption:
 architectural parameters
 functional parameters
 technological parameters
 Static power estimation model.
isoTISOPTISOP
TregRCPcmbP
LRISOLRPLRP
iONOFFlkgiONONlkg
iON
LRactors
lkglkg
ioverheadilkgONilkg
i
)]*#1(*)(*)([
*])*#()([
)()()(




ReConFig2015-Dec7-92015,MayanRiviera(Mexico)–TizianaFanni
6

10. STATIC POWER ESTIMATION MODEL
 Parameters determining the power consumption:
 architectural parameters
 functional parameters
 technological parameters
 Static power estimation model.
isoTISOPTISOP
TregRCPcmbP
LRISOLRPLRP
iONOFFlkgiONONlkg
iON
LRactors
lkglkg
ioverheadilkgONilkg
i
)]*#1(*)(*)([
*])*#()([
)()()(




ReConFig2015-Dec7-92015,MayanRiviera(Mexico)–TizianaFanni
6

11. STATIC POWER ESTIMATION MODEL
 Parameters determining the power consumption:
 architectural parameters
 functional parameters
 technological parameters
 Static power estimation model.
isoTISOPTISOP
TregRCPcmbP
LRISOLRPLRP
iONOFFlkgiONONlkg
iON
LRactors
lkglkg
ioverheadilkgONilkg
i
)]*#1(*)(*)([
*])*#()([
)()()(




ReConFig2015-Dec7-92015,MayanRiviera(Mexico)–TizianaFanni
6

12. PHD PLAN
PHD started on 1th October 2015
Dynamic model: a similar study
about the dynamic power consumption
should be executed, in order to derive a
dynamic power estimation model.
Asses technology impact: different
technologies need to be explored, to
check whether the proposed model
need to be adjusted.
Automate the strategy: integration
of the proposed parameter analysis and
estimation model within the MDC
automatic flow.
ReConFig2015-Dec7-92015,MayanRiviera(Mexico)–TizianaFanni
7

13. ACKNOWLEDGEMENTS
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8
The research leading to these results has received funding
from:
 the Region of Sardinia
L.R.7/2007 under grant agreement
CRP-18324 [RPCT Project].
 the Region of Sardinia
L.R.7/2007 under grant agreement
CRP-60544 [ELoRA Project].

14. Francesca Palumbo
University of Sassari
PolComIng
Information Engineering Unit
Tiziana Fanni, Carlo Sau, Paolo Meloni
and Luigi Raffo
University of Cagliari
Dept. of Electrical and Electronics Eng.
tiziana.fanni@unica.it.
POWER MODELLING FOR
SAVING STRATEGIES IN
COARSE GRAINED
RECONFIGURABLE SYSTEMS
E
O
L
A
B
?
2015 International Conference on
ReConFigurable Computing and FPGAs

15. READ FULL ARTICLE
 IEEE Xplore Digital Library
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&a
rnumber=7393337
 RPCT Project Website:
http://sites.unica.it/rpct/references/
14

16. REFERENCE
 @inproceedings{FANNI_Reconfig2016,
author = {Tiziana Fanni and Carlo Sau and
Paolo Meloni and Luigi Raffo and Francesca
Palumbo},
title = {Power modelling for saving strategies in
coarse grained reconfigurable systems},
booktitle = {International Conference on
ReConFigurable Computing and FPGAs,
ReConFig 2015,
Riviera Maya, Mexico, December 7-9, 2015},
pages = {1--4},
year = {2015},
doi = {10.1109/ReConFig.2015.7393337}}
ReConFig2015-Dec7-9,MayanRiviera(Mexico)-TizianaFanni
BibTex