I
nte
rna
t
io
na
l J
o
urna
l o
f
E
lect
rica
l a
nd
Co
m
p
ute
r
E
ng
in
ee
ring
(
I
J
E
CE
)
Vo
l.
10
,
No
.
6
,
Dec
em
b
er
2
0
2
0
,
p
p
.
6
5
4
9
~
6
557
I
SS
N:
2
0
8
8
-
8708
,
DOI
: 1
0
.
1
1
5
9
1
/
i
j
ec
e
.
v
1
0
i
6
.
pp
6
5
4
9
-
6
5
5
7
6549
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ij
ec
e.
ia
esco
r
e.
co
m/in
d
ex
.
p
h
p
/I
JE
C
E
M
ulti
-
o
bje
ctive
P
a
reto f
ro
nt
a
nd
p
a
rticle
sw
a
rm
opt
i
m
i
z
a
tion
a
lg
o
rith
m
s
for
p
o
w
er
d
iss
ipa
tion
r
e
duction in
m
icro
p
ro
cess
o
rs
Dia
ry
R.
Su
la
i
m
a
n
De
p
a
rtme
n
t
o
f
El
e
c
tri
c
a
l
En
g
in
e
e
rin
g
,
Co
ll
e
g
e
o
f
En
g
in
e
e
rin
g
,
S
a
lah
a
d
d
i
n
Un
iv
e
rsity
-
Erb
il
,
Ira
q
Art
icle
I
nfo
AB
ST
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
Mar
25
,
2
0
20
R
ev
i
s
ed
Ma
y
31
,
2020
A
cc
ep
ted
J
u
n
1
6
,
2
0
2
0
T
h
e
p
ro
g
re
ss
o
f
m
icro
e
lec
tro
n
ics
m
a
k
in
g
p
o
ss
ib
le
h
ig
h
e
r
in
teg
ra
ti
o
n
d
e
n
siti
e
s,
a
n
d
a
c
o
n
si
d
e
ra
b
le
d
e
v
e
lo
p
m
e
n
t
o
f
o
n
-
b
o
a
rd
sy
ste
m
s
a
r
e
c
u
rre
n
tl
y
u
n
d
e
rg
o
i
n
g
,
t
h
is
g
ro
w
th
c
o
m
e
s
u
p
a
g
a
in
st
a
li
m
it
in
g
f
a
c
to
r
o
f
p
o
w
e
r
d
issip
a
ti
o
n
.
Hig
h
e
r
p
o
w
e
r
d
issip
a
ti
o
n
w
il
l
c
a
u
se
a
n
im
m
e
d
iate
sp
re
a
d
o
f
g
e
n
e
ra
ted
h
e
a
t
w
h
ich
c
a
u
se
s
th
e
rm
a
l
p
ro
b
lem
s.
Co
n
se
q
u
e
n
tl
y
,
th
e
s
y
ste
m
'
s
to
tal
c
o
n
s
u
m
e
d
e
n
e
rg
y
w
il
l
in
c
re
a
se
a
s
th
e
s
y
ste
m
te
m
p
e
ra
tu
r
e
in
c
re
a
se
.
Hig
h
te
m
p
e
ra
tu
re
s
in
m
icro
p
ro
c
e
ss
o
rs
a
n
d
larg
e
th
e
rm
a
l
e
n
e
rg
y
o
f
c
o
m
p
u
ter
s
y
ste
m
s
p
ro
d
u
c
e
h
u
g
e
p
r
o
b
le
m
s
o
f
s
y
st
e
m
c
o
n
f
id
e
n
c
e
,
p
e
rf
o
r
m
a
n
c
e
,
a
n
d
c
o
o
li
n
g
e
x
p
e
n
se
s.
P
o
w
e
r
c
o
n
su
m
e
d
b
y
p
ro
c
e
ss
o
rs
a
re
m
a
i
n
ly
d
u
e
t
o
th
e
in
c
re
a
se
in
n
u
m
b
e
r
o
f
c
o
re
s
a
n
d
t
h
e
c
lo
c
k
f
re
q
u
e
n
c
y
,
w
h
ich
is
d
issip
a
ted
in
t
h
e
f
o
rm
o
f
h
e
a
t
a
n
d
c
a
u
se
s
th
e
rm
a
l
c
h
a
ll
e
n
g
e
s
f
o
r
c
h
ip
d
e
sig
n
e
rs.
A
s
th
e
m
icro
p
ro
c
e
ss
o
r’s
p
e
rfo
rm
a
n
c
e
h
a
s
in
c
re
a
s
e
d
re
m
a
r
k
a
b
l
y
in
Na
n
o
-
m
e
ter
t
e
c
h
n
o
lo
g
y
,
p
o
w
e
r
d
issip
a
ti
o
n
is
b
e
c
o
m
in
g
n
o
n
-
n
e
g
li
g
ib
le.
T
o
so
lv
e
th
is
p
ro
b
lem
,
th
is
a
rti
c
le
a
d
d
re
ss
e
s
p
o
we
r
d
issip
a
ti
o
n
re
d
u
c
ti
o
n
issu
e
s
f
o
r
h
ig
h
p
e
rf
o
rm
a
n
c
e
p
ro
c
e
ss
o
rs
u
sin
g
m
u
lti
-
o
b
jec
ti
v
e
P
a
re
to
f
ro
n
t
(P
F
),
a
n
d
p
a
rti
c
le
sw
a
r
m
o
p
ti
m
iza
ti
o
n
(
P
S
O)
a
lg
o
rit
h
m
s
to
a
c
h
i
e
v
e
p
o
w
e
r
d
issip
a
ti
o
n
a
s
a
p
rio
r
c
o
m
p
u
tati
o
n
th
a
t
re
d
u
c
e
s
th
e
re
a
l
d
e
lay
o
f
a
targ
e
t
m
icro
p
ro
c
e
ss
o
r
u
n
it
.
S
im
u
latio
n
i
s
v
e
ri
f
ied
th
e
c
o
n
c
e
p
tu
a
l
f
u
n
d
a
m
e
n
tals
a
n
d
o
p
ti
m
i
z
a
ti
o
n
o
f
jo
in
t
b
o
d
y
a
n
d
su
p
p
ly
v
o
lt
a
g
e
s
(V
th
-
V
DD
)
w
h
ich
sh
o
w
in
g
sa
ti
sfa
c
to
r
y
f
in
d
in
g
s.
K
ey
w
o
r
d
s
:
Op
ti
m
izatio
n
t
ec
h
n
iq
u
es
P
ar
eto
f
r
o
n
t
P
ar
ticle
s
w
ar
m
o
p
ti
m
izatio
n
P
o
r
tab
le
p
r
o
ce
s
s
o
r
s
P
o
w
er
d
is
s
ip
atio
n
Co
p
y
rig
h
t
©
2
0
2
0
In
stit
u
te o
f
A
d
v
a
n
c
e
d
E
n
g
i
n
e
e
rin
g
a
n
d
S
c
ien
c
e
.
Al
l
rig
h
ts re
se
rv
e
d
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Diar
y
R
.
S
u
lai
m
a
n
,
Dep
ar
t
m
en
t o
f
E
lectr
ical
E
n
g
i
n
ee
r
in
g
,
C
o
lle
g
e
o
f
E
n
g
i
n
ee
r
i
n
g
,
Salah
ad
d
in
U
n
i
v
er
s
it
y
-
E
r
b
il
,
E
r
b
il,
Ku
r
d
is
tan
R
e
g
io
n
,
I
r
aq
.
E
m
ail:
d
iar
i
y
@
g
m
ail.
co
m
,
d
ia
r
y
.
s
u
lai
m
a
n
@
s
u
.
ed
u
.
k
r
d
1.
I
NT
RO
D
UCT
I
O
N
T
h
e
p
er
f
o
r
m
a
n
ce
g
r
o
w
t
h
o
f
d
ig
ital
s
y
s
te
m
s
,
a
n
d
m
ass
iv
e
d
ev
elo
p
m
e
n
t
in
Na
n
o
-
m
eter
te
ch
n
o
lo
g
ie
s
ar
e
co
m
i
n
g
b
ec
au
s
e
o
f
t
h
e
e
x
ten
s
i
v
e
i
n
tr
o
d
u
ctio
n
o
f
elec
tr
o
n
ics
a
n
d
p
o
r
tab
le
d
ev
ices
in
to
d
aily
l
if
e.
T
o
d
ay
,
w
e
ar
e
co
n
s
id
er
in
g
co
m
p
le
x
ch
ip
s
co
m
p
r
is
in
g
h
i
g
h
le
v
el
o
f
p
o
w
er
d
is
s
ip
atio
n
p
l
u
s
g
e
n
er
ated
h
ea
t
,
an
d
th
er
e
f
o
r
e
n
ee
d
to
b
e
in
li
n
e
w
i
th
th
e
r
ed
u
c
tio
n
i
n
t
h
e
d
i
m
en
s
io
n
s
o
f
m
icr
o
elec
tr
o
n
ic
s
an
d
d
ig
ital
d
ev
ices.
C
u
r
r
en
tl
y
,
co
m
m
er
cial
m
icr
o
p
r
o
ce
s
s
o
r
cir
cu
its
ar
e
av
ailab
le
w
it
h
C
MO
S
tr
an
s
is
to
r
s
w
i
th
a
later
al
s
ize
in
Nan
o
-
m
eter
p
r
o
ce
s
s
tec
h
n
o
lo
g
y
,
s
u
ch
m
i
n
iat
u
r
izatio
n
h
a
s
le
d
to
en
o
r
m
o
u
s
p
o
w
er
an
d
te
m
p
er
atu
r
e
ch
alle
n
g
e
s
w
it
h
t
h
e
p
r
esen
ce
o
f
b
illi
o
n
s
o
f
tr
an
s
is
to
r
s
o
n
t
h
e
ch
ip
[
1
,
2
]
.
Hig
h
er
p
o
w
er
d
is
s
ip
atio
n
lea
d
s
to
in
cr
ea
s
e
ch
ip
te
m
p
er
atu
r
e
le
v
els
th
a
t
co
m
p
r
o
m
i
s
i
n
g
th
e
li
f
e
o
f
m
icr
o
p
r
o
ce
s
s
o
r
s
d
u
e
to
t
h
e
ad
d
itio
n
o
f
n
e
w
f
ea
tu
r
e
s
an
d
p
er
f
o
r
m
a
n
ce
.
T
h
is
tr
en
d
is
i
n
o
p
p
o
s
itio
n
to
an
in
teg
r
atio
n
in
m
icr
o
elec
tr
o
n
ics
w
h
ich
tr
ie
s
to
b
e
as
co
m
p
ac
t
an
d
as
au
to
n
o
m
o
u
s
as
p
o
s
s
ib
le
f
o
r
p
o
r
tab
le
ap
p
licatio
n
s
.
T
h
is
in
tr
o
d
u
ctio
n
o
f
h
ig
h
p
er
f
o
r
m
a
n
ce
an
d
n
e
w
f
ea
t
u
r
es
w
ill t
h
er
ef
o
r
e
o
n
l
y
b
e
d
o
n
e
w
it
h
s
i
g
n
i
f
ican
t c
o
o
li
n
g
tech
n
o
lo
g
y
o
r
f
u
n
d
a
m
e
n
tal
d
esig
n
ch
a
n
g
es [
3
]
.
Mo
r
eo
v
er
,
d
elay
o
f
t
h
e
m
icr
o
p
r
o
ce
s
s
o
r
’
s
ch
ip
n
ee
d
s
to
m
ee
t
t
h
e
tar
g
et
p
er
f
o
r
m
a
n
ce
an
d
lo
w
e
s
t
p
o
w
er
d
is
s
ip
atio
n
.
Usi
n
g
m
u
lti
-
o
b
j
ec
tiv
e
p
ar
ticle
s
w
ar
m
o
p
tim
izatio
n
(
P
SO
)
is
a
v
er
y
ef
f
icie
n
t
ap
p
r
o
ac
h
f
o
r
ac
h
ie
v
i
n
g
P
ar
eto
f
r
o
n
t
(
P
F)
d
ec
is
io
n
s
f
o
r
m
icr
o
p
r
o
ce
s
s
o
r
’
s
h
i
g
h
-
p
o
w
er
d
is
s
ip
at
io
n
p
r
o
b
lem
s
[
4
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
10
,
No
.
6
,
Dec
em
b
er
2
0
2
0
:
6
549
-
6
5
5
7
6550
Fro
m
m
icr
o
p
r
o
ce
s
s
o
r
s
p
o
w
er
co
n
s
u
m
p
t
io
n
r
i
g
o
r
o
u
s
s
u
r
v
e
y
o
f
r
elate
d
w
o
r
k
s
an
d
p
u
b
lis
h
ed
s
tu
d
ie
s
,
it
is
o
b
s
er
v
ed
th
at,
th
er
e
h
a
v
e
b
e
en
n
u
m
er
o
u
s
ar
ticle
s
,
a
n
d
p
u
b
lis
h
ed
s
tu
d
ie
s
o
n
t
h
e
d
esi
g
n
a
n
d
o
p
ti
m
iza
tio
n
tech
n
iq
u
es.
T
o
th
is
en
d
,
n
u
m
b
er
o
f
n
e
w
w
o
r
k
s
p
u
b
li
s
h
ed
r
ec
en
tl
y
h
a
s
b
ee
n
p
o
in
ted
o
u
t.
Sh
ei
k
h
,
et
al.
,
p
r
o
p
o
s
ed
a
m
u
l
ti
-
o
b
j
ec
tiv
e
m
u
tat
iv
e
a
lg
o
r
it
h
m
b
ased
o
n
tas
k
s
c
h
ed
u
li
n
g
a
p
p
r
o
ac
h
f
o
r
r
eso
lv
in
g
P
ar
eto
o
p
tim
al
s
o
l
u
tio
n
s
(
P
OS)
w
it
h
s
i
m
u
lta
n
eo
u
s
o
p
ti
m
izatio
n
o
f
te
m
p
er
atu
r
e
,
p
er
f
o
r
m
a
n
ce
,
an
d
en
er
g
y
.
I
n
ad
d
itio
n
,
t
h
e
y
p
r
esen
t
a
m
et
h
o
d
o
lo
g
y
to
c
h
o
o
s
e
a
s
i
n
g
le
s
o
lu
tio
n
f
r
o
m
P
F
g
i
v
en
th
e
u
s
er
's
p
r
ef
er
en
ce
.
T
h
e
p
r
o
p
o
s
ed
a
lg
o
r
ith
m
f
o
r
s
c
h
ed
u
li
n
g
tas
k
s
ac
h
ie
v
es
t
h
r
ee
-
w
a
y
o
p
tim
izatio
n
w
it
h
f
as
t
tu
r
n
ar
o
u
n
d
ti
m
e,
a
n
d
i
s
ad
v
an
tag
eo
u
s
b
ec
au
s
e
it
r
ed
u
ce
s
en
er
g
y
as
w
ell
a
s
te
m
p
er
at
u
r
es
r
at
h
er
t
h
an
i
n
is
o
latio
n
[
5
]
.
Van
ap
alli
in
tr
o
d
u
ce
s
P
SO
alg
o
r
ith
m
f
o
r
VL
SI
s
y
s
te
m
s
to
r
ed
u
ce
leak
ag
e
p
o
w
er
d
is
s
ip
atio
n
b
ased
o
n
leak
ag
e
v
ec
to
r
.
I
n
th
is
p
ap
er
,
th
e
g
en
etic
al
g
o
r
ith
m
(
GA
)
is
p
r
esen
ted
b
r
ief
l
y
a
n
d
also
i
m
p
le
m
e
n
ted
to
s
ea
r
c
h
f
o
r
m
i
n
i
m
u
m
lea
k
ag
e
v
ec
to
r
(
ML
V)
an
d
co
m
p
ar
e
d
w
it
h
P
SO
i
n
ter
m
s
o
f
t
i
m
e
d
ela
y
a
n
d
n
u
m
b
er
o
f
iter
atio
n
s
,
t
h
e
p
r
o
p
o
s
ed
a
p
p
r
o
ac
h
is
s
i
m
u
lated
an
d
v
er
id
f
ied
o
n
a
n
u
m
b
er
o
f
cir
cu
i
ts
as a
ca
s
e
s
t
u
d
y
[
6
]
.
A
tt
ia,
et
al.
,
a
n
al
y
ze
d
t
h
e
b
a
s
ic
t
h
eo
r
ies
o
f
m
u
lti
-
co
r
e,
tr
en
d
in
g
r
esear
c
h
ar
ea
s
f
o
r
o
f
m
u
lti
-
co
r
e
m
icr
o
p
r
o
ce
s
s
o
r
s
an
d
th
e
n
f
o
cu
s
ed
o
n
e
n
er
g
y
m
a
n
ag
e
m
e
n
t
p
r
o
b
le
m
is
s
u
e
s
i
n
m
u
lti
-
co
r
e
ar
ch
itectu
r
e
s
.
Mo
r
eo
v
er
,
th
ey
d
is
c
u
s
s
ed
th
e
d
if
f
er
e
n
t
tech
n
iq
u
e
s
f
o
r
p
o
w
er
m
a
n
a
g
e
m
en
t,
an
d
p
r
o
p
o
s
ed
a
s
p
ec
if
ic
tech
n
iq
u
e
f
o
r
p
o
w
er
m
a
n
a
g
e
m
e
n
t
in
m
u
lti
-
co
r
e
p
r
o
ce
s
s
o
r
s
b
ased
o
n
th
at
s
u
r
v
e
y
[
7
]
.
S
u
lai
m
a
n
,
e
t
al.
,
p
r
o
p
o
s
ed
an
o
p
tim
a
l
co
n
cu
r
r
e
n
t
j
o
in
t
s
et
o
f
th
e
s
u
p
p
l
y
an
d
t
h
r
es
h
o
ld
v
o
ltag
e
s
ca
lin
g
(
V
th
-
V
DD
)
f
o
r
m
in
i
m
izin
g
p
o
w
er
d
is
s
ip
atio
n
o
f
m
o
d
er
n
h
i
g
h
-
s
p
ee
d
d
ig
ital
s
y
s
te
m
s
.
I
n
o
r
d
e
r
to
v
alid
ate
m
i
n
i
m
u
m
p
o
w
e
r
d
is
s
ip
atio
n
,
t
h
e
y
test
ed
v
ar
io
u
s
V
th
-
V
DD
s
et
s
b
a
s
ed
o
n
P
F
a
n
d
P
SO
al
g
o
r
i
th
m
s
.
T
h
eir
r
es
u
lt
s
v
er
if
ied
o
n
a
h
i
g
h
-
p
er
f
o
r
m
an
ce
p
r
o
ce
s
s
o
r
f
o
r
m
i
n
i
m
u
m
p
o
w
er
r
ed
u
c
tio
n
lev
e
ls
,
m
i
n
i
m
u
m
te
m
p
er
atu
r
e
le
v
el
s
,
a
n
d
m
u
ltip
le
w
o
r
k
lo
ad
co
n
d
itio
n
s
[
8
]
.
A
.
K
u
m
ar
,
an
d
R
.
K.
Nag
ar
ia
p
r
o
p
o
s
ed
an
d
d
ev
elo
p
ed
d
o
m
in
o
g
ate
as
a
n
e
w
lea
k
a
g
e
to
ler
an
t
h
ig
h
s
p
ee
d
w
h
ic
h
h
a
s
h
i
g
h
er
n
o
is
e
i
m
m
u
n
it
y
,
lo
w
er
p
o
w
er
d
is
s
ip
atio
n
,
a
n
d
les
s
p
r
o
ce
s
s
v
ar
iatio
n
s
f
o
r
w
id
e
f
an
-
i
n
O
R
lo
g
ic
g
ate.
Fu
r
t
h
er
m
o
r
e,
s
tac
k
i
n
g
o
f
NM
O
S
tr
a
n
s
is
to
r
s
is
ac
co
m
p
lis
h
ed
to
r
ed
u
ce
leak
a
g
e
p
o
w
er
co
n
s
u
m
p
tio
n
an
d
to
tal
cu
r
r
en
t
tr
an
s
f
er
i
n
ca
s
ca
d
e
f
as
h
io
n
th
at
ca
n
b
e
o
p
er
ated
in
d
ee
p
s
u
b
m
icr
o
n
p
r
o
ce
s
s
tech
n
o
lo
g
y
[
9
]
.
Sin
g
h
et
al.
,
p
r
ese
n
ted
a
1
0
T
s
tatic
r
a
n
d
o
m
-
ac
ce
s
s
m
e
m
o
r
y
(
SR
A
M)
ce
l
l
to
i
m
p
r
o
v
e
l
ea
k
ag
e
p
o
w
e
r
d
is
s
ip
atio
n
w
it
h
i
m
p
r
o
v
ed
ce
l
l
s
tab
ilit
y
.
T
h
e
p
r
o
p
o
s
ed
SR
A
M
ce
ll
is
ad
o
p
te
d
to
d
esig
n
a
lo
o
k
u
p
tab
le
f
o
r
6
-
in
p
u
ts
o
f
FP
G
A
a
n
d
a
2
k
b
SR
A
M
m
ac
r
o
b
lo
ck
.
T
h
e
y
ac
h
iev
ed
s
u
p
er
io
r
r
es
u
lts
in
ter
m
s
o
f
w
r
ite
a
n
d
r
ea
d
s
tatic
n
o
is
e
m
ar
g
in
s
;
a
n
d
le
s
s
lea
k
ag
e
p
o
w
er
d
is
s
ip
atio
n
[
1
0
]
.
Y.
W
an
g
,
et
al.
p
r
o
p
o
s
ed
h
y
p
o
th
e
s
e
s
f
o
r
th
e
u
n
d
er
l
y
i
n
g
ca
u
s
es
an
d
v
alid
ate
p
o
w
er
v
ar
iatio
n
s
i
n
p
r
o
ce
s
s
o
r
s
b
ased
u
p
o
n
s
p
e
cif
icall
y
g
o
v
er
n
ed
en
v
ir
o
n
m
e
n
tal
f
ac
to
r
s
.
T
h
eir
t
est
f
i
n
d
in
g
s
in
d
icate
th
a
t,
t
h
r
o
u
g
h
in
cr
ea
s
e
o
f
n
u
m
b
er
o
f
t
r
a
n
s
i
s
to
r
s
,
v
ar
ian
ce
o
f
te
m
p
er
atu
r
e
f
ea
t
u
r
es
b
ec
o
m
e
s
h
i
g
h
er
w
i
th
in
p
r
o
ce
s
s
o
r
s
,
w
h
er
ea
t
h
as
i
m
p
o
r
tan
t
in
v
o
l
v
e
m
en
t
to
t
h
e
c
h
an
g
e
i
n
p
o
w
er
d
is
s
ip
atio
n
f
o
r
p
r
esen
t
p
r
o
ce
s
s
o
r
s
[
1
1
]
.
E
.
A
n
g
el,
et
a
l.
s
tu
d
ied
is
s
u
e
o
f
s
c
h
ed
u
led
s
et
o
f
j
o
b
s
w
it
h
ti
m
e,
ti
m
eli
n
e
s
an
d
h
a
n
d
li
n
g
p
r
o
ce
s
s
d
e
m
an
d
s
to
r
ed
u
ce
th
e
to
tal
p
o
w
er
d
is
s
ip
atio
n
o
n
a
p
ar
allel
s
ca
lab
le
p
r
o
ce
s
s
o
r
.
T
h
ey
d
er
iv
ed
t
h
e
i
s
s
u
e
a
s
a
co
n
v
e
x
p
r
o
g
r
a
m
a
n
d
p
r
esen
ted
a
co
m
b
in
a
to
r
ial
p
o
l
y
n
o
m
ial
ti
m
e
ap
p
r
o
ac
h
w
h
ic
h
is
b
ased
o
n
f
i
n
d
in
g
m
ax
i
m
u
m
f
lo
w
s
[
1
2
]
.
T
h
is
p
ap
er
p
r
esen
ts
P
F
an
d
P
SO
o
p
tim
izat
io
n
al
g
o
r
ith
m
s
to
e
n
s
u
r
e
th
e
e
f
f
icien
t
o
p
er
atio
n
o
f
d
y
n
a
m
ic
v
o
ltag
e
s
ca
li
n
g
(
D
V
S)
an
d
B
o
d
y
B
ias
Vo
lta
g
e
s
ca
lin
g
(
B
B
VS)
f
o
r
p
o
w
er
d
is
s
ip
atio
n
m
in
i
m
iza
tio
n
in
m
ic
r
o
p
r
o
ce
s
s
o
r
s
.
T
h
e
co
m
b
in
ed
DVS
an
d
B
B
VS
s
ca
li
n
g
tec
h
n
iq
u
e
i
s
d
y
n
a
m
ica
ll
y
alter
in
g
p
r
o
ce
s
s
o
r
's
th
r
o
u
g
h
p
u
t
f
o
r
en
er
g
y
-
e
f
f
ici
en
c
y
.
Am
o
n
g
,
n
u
m
b
er
o
f
p
ar
am
eter
s
ar
e
co
n
s
id
er
ed
f
o
r
p
o
s
s
ib
le
p
o
w
er
d
is
s
ip
atio
n
i
m
p
r
o
v
e
m
e
n
t
b
y
s
ca
li
n
g
s
u
p
p
l
y
v
o
lta
g
e,
f
r
e
q
u
en
c
y
,
as
w
ell
as
th
r
e
s
h
o
l
d
v
o
ltag
e
(
V
th
)
o
f
a
h
i
g
h
-
p
er
f
o
r
m
an
ce
p
o
r
tab
le
p
r
o
ce
s
s
o
r
a
s
a
ca
s
e
s
t
u
d
y
.
S
i
m
u
latio
n
r
esu
lts
ar
e
u
s
ed
to
v
alid
ate
t
h
eo
r
etica
l
b
asics
an
d
P
F
-
P
SO
o
p
ti
m
iz
atio
n
o
f
th
r
e
s
h
o
ld
-
s
u
p
p
l
y
v
o
ltag
e
s
ca
li
n
g
(
V
th
-
V
DD
)
ap
p
r
o
ac
h
th
at
s
h
o
w
s
s
atis
f
ac
to
r
y
r
e
s
u
l
ts
.
Nev
er
t
h
e
less
,
t
h
e
s
tu
d
y
co
u
ld
b
e
ap
p
li
ed
f
o
r
s
y
s
te
m
le
v
el
p
o
w
er
est
i
m
atio
n
f
o
r
v
ar
io
u
s
t
y
p
es o
f
h
i
g
h
-
p
er
f
o
r
m
a
n
ce
p
o
r
tab
le
s
y
s
te
m
s
.
2.
DYNA
M
I
C
VO
L
T
A
G
E
SC
AL
I
N
G
(
DV
S)
AND
B
O
DY
B
I
AS V
O
L
T
A
G
E
S
CAL
I
N
G
(
B
B
VS)
Hig
h
-
p
er
f
o
r
m
a
n
ce
p
o
r
tab
le
p
r
o
ce
s
s
o
r
’
s
g
r
o
w
t
h
co
m
i
n
g
u
p
a
g
ai
n
s
t
m
in
i
m
iza
tio
n
o
f
p
o
w
e
r
co
n
s
u
m
p
tio
n
ch
alle
n
g
es
as
k
e
y
o
b
s
tacle
s
i
n
th
e
d
es
ig
n
i
n
s
p
ite
o
f
h
ea
t
d
is
s
i
p
atio
n
c
h
alle
n
g
e
s
th
a
t
b
ec
o
m
in
g
a
co
n
s
tr
ain
t
in
ter
m
s
o
f
to
tal
en
er
g
y
co
n
s
u
m
p
tio
n
o
f
p
r
o
ce
s
s
o
r
s
,
th
i
s
is
a
m
aj
o
r
is
s
u
e
i
n
d
ig
ital
a
n
d
p
o
r
tab
l
e
ap
p
licatio
n
s
an
d
s
h
o
u
ld
b
e
co
n
s
id
er
ed
at
ea
ch
cir
cu
it
lev
el
d
esig
n
.
T
h
e
elec
tr
ical
p
o
w
er
co
n
s
u
m
ed
in
C
MO
S
cir
cu
its
is
m
ai
n
l
y
d
iv
id
ed
i
n
to
t
w
o
co
m
p
o
n
e
n
ts
:
a
d
y
n
a
m
ic
p
o
w
er
d
u
e
to
t
h
e
s
w
i
tch
i
n
g
ac
tiv
it
y
o
f
th
e
tr
an
s
is
to
r
s
,
an
d
a
s
tat
ic
p
o
w
er
d
u
e
to
th
e
lea
k
ag
e
c
u
r
r
en
t
s
.
T
h
e
m
o
d
er
n
C
M
OS
tec
h
n
o
l
o
g
y
s
ca
l
in
g
ca
u
s
es
an
e
x
p
o
n
en
tia
l
g
r
o
w
t
h
o
f
b
o
th
s
ta
tic
a
n
d
d
y
n
a
m
ic
p
o
w
er
d
is
s
ip
atio
n
s
.
T
h
u
s
,
th
e
s
e
t
w
o
c
o
m
p
o
n
en
t
s
m
u
s
t
b
e
co
n
s
id
er
ed
w
h
e
n
o
p
ti
m
iz
in
g
p
o
w
er
d
is
s
ip
atio
n
.
T
h
e
s
u
p
p
ly
a
n
d
th
r
es
h
o
ld
v
o
ltag
e
e
x
te
n
t
to
d
ec
r
ea
s
e
f
o
r
m
ai
n
tai
n
in
g
h
i
g
h
e
s
t
p
er
f
o
r
m
an
ce
an
d
lo
w
e
s
t
p
o
w
er
r
eq
u
ir
e
m
e
n
t
s
.
T
o
tal
p
o
w
er
(
P
tot
)
,
d
y
n
a
m
ic
p
o
w
er
(P
dynam
ic
)
,
an
d
s
tatic
p
o
w
er
(
P
static
)
d
is
s
ip
atio
n
i
n
a
C
MO
S c
ir
cu
it is
g
i
v
en
b
y
[
1
3
,
1
4
]
,
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2
0
8
8
-
8708
Mu
lti
-
o
b
jective
P
F
a
n
d
P
S
O
a
lg
o
r
ith
ms fo
r
p
o
w
er
.
..
(
Dia
r
y
R
.
S
u
la
ima
n
)
6551
22
1
(
1
)
2
gs
th
ds
TT
VV
V
m
V
V
ox
t
ot
L
DD
c
l
k
T
WC
P
C
V
f
V
e
e
L
(
1
)
2
1
2
d
y
n
a
m
ic
L
D
D
c
lk
P
C
V
f
(
2
)
2
(
1
)
gs
th
ds
TT
VV
V
m
V
V
ox
st
at
i
c
l
e
ak
age
DD
T
WC
P
I
V
V
e
e
L
(
3
)
w
h
er
e,
α
is
th
e
n
o
d
e
tr
a
n
s
it
io
n
ac
ti
v
it
y
f
ac
to
r
,
C
L
i
s
t
h
e
to
tal
lo
ad
ca
p
ac
itan
ce
,
V
DD
is
t
h
e
s
u
p
p
l
y
v
o
lta
g
e,
an
d
f
clk
is
t
h
e
clo
ck
f
r
eq
u
e
n
c
y
.
µ
is
t
h
e
ca
r
r
ier
m
o
b
ilit
y
,
W
is
t
h
e
tr
an
s
is
to
r
ch
a
n
n
el
w
id
t
h
,
C
ox
i
s
o
x
id
e
ca
p
ac
itan
ce
p
er
u
n
it
ar
ea
,
V
T
is
t
h
e
th
er
m
al,
V
gs
i
s
t
h
e
g
ate
-
to
-
s
o
u
r
ce
v
o
ltag
e,
V
th
i
s
t
h
r
e
s
h
o
ld
v
o
lta
g
e,
m
i
s
s
u
b
t
h
r
esh
o
ld
s
w
i
n
g
co
ef
f
icie
n
t,
an
d
V
ds
is
th
e
d
r
ain
-
to
-
s
o
u
r
c
e
v
o
lta
g
e
[
1
5
]
.
T
h
e
th
r
esh
o
ld
v
o
ltag
e
o
f
a
C
MO
S
tr
an
s
i
s
to
r
ca
n
b
e
g
iv
e
n
b
y
,
D
I
B
L
D
D
N
W
(
)
V
th
tho
s
bs
s
V
V
V
(
4
)
w
h
er
e,
V
tho
is
t
h
e
Z
er
o
B
iased
th
r
es
h
o
ld
v
o
ltag
e
(
V
bs
=
0
V)
,
th
e
p
ar
a
m
eter
s
s
,
γ
,
DIBL
ar
e
c
o
n
s
ta
n
t
f
o
r
a
g
iv
e
n
tech
n
o
lo
g
y
ca
lled
co
ef
f
icie
n
t
o
f
b
o
d
y
ef
f
ec
t,
V
bs
is
t
h
e
b
o
d
y
s
o
u
r
ce
v
o
lta
g
e,
NW
i
s
a
co
n
s
ta
n
t
th
at
m
o
d
els
n
ar
r
o
w
w
id
t
h
e
f
f
ec
ts
,
a
n
d
V
DD
is
t
h
e
s
u
p
p
l
y
v
o
lta
g
e.
b
s
s
V
,
th
e
n
,
s
b
s
Vs
ca
n
b
e
li
n
ea
r
ized
as
K
.
V
bs
,
th
en
V
th
b
ec
o
m
e
s
[
1
6
]
,
1
1
1
.
.
(
.
&
)
,
.
th
th
D
D
b
s
D
D
to
b
s
B
B
th
to
b
s
V
V
K
V
K
V
K
V
V
V
V
th
e
n
V
V
K
V
(
5
)
w
h
er
e,
V
tho
,
K
1
,
an
d
K
ar
e
co
n
s
ta
n
t
s
.
I
t
is
o
b
v
io
u
s
t
h
at,
th
e
th
r
es
h
o
ld
v
o
ltag
e
(
V
th
)
h
as
a
lin
ea
r
r
elatio
n
w
i
t
h
V
bs
an
d
V
DD
.
T
h
is
ch
a
n
g
e
i
n
th
r
es
h
o
ld
v
o
ltag
e
f
r
o
m
it
s
n
o
m
in
al
v
alu
e
V
tho
d
u
e
to
V
bs
is
ca
lled
b
o
d
y
b
ia
s
v
o
ltag
e
(
V
BB
)
,
V
BBN
is
a
n
e
g
a
tiv
e
v
o
ltag
e
t
h
at
t
y
p
icall
y
u
s
e
d
f
o
r
NM
OS
tr
an
s
is
to
r
s
,
a
n
d
V
BBP
is
ap
p
o
s
iti
v
e
v
o
ltag
e
th
at
u
s
ed
f
o
r
P
MO
S
tr
an
s
i
s
to
r
s
.
P
r
in
cip
ally
,
P
M
OS
an
d
NM
O
S
ar
e
d
esi
g
n
ed
to
i
m
p
le
m
en
t
th
eir
b
alan
ce
ch
ar
ac
ter
is
tic
s
.
T
h
er
ef
o
r
e
[
1
7
]
,
B
B
N
B
B
P
D
D
V
V
V
(
6
)
w
h
er
e,
V
BBN
an
d
V
BBP
ar
e
NM
OS a
n
d
P
MO
S b
o
d
y
b
ias
v
o
lt
ag
es r
esp
ec
ti
v
el
y
.
W
h
en
h
i
g
h
s
p
ee
d
an
d
lo
w
lat
en
c
y
ar
e
d
esire
d
,
V
th
is
r
ed
u
c
ed
u
s
in
g
f
o
r
w
ar
d
b
o
d
y
b
iasi
n
g
(
FB
B
)
.
Fo
r
lo
w
er
w
o
r
k
lo
ad
,
th
i
s
s
c
h
e
m
e
s
lo
w
s
d
o
w
n
t
h
e
cir
cu
it
b
y
in
cr
ea
s
i
n
g
V
th
t
h
r
o
u
g
h
r
e
v
er
s
e
b
o
d
y
b
iasi
n
g
(
R
B
B
)
as
s
h
o
w
n
i
n
Fi
g
u
r
e
1
[
1
8
]
.
I
t
is
im
p
o
r
tan
t
to
p
o
in
t
o
u
t
th
at,
th
e
o
n
l
y
w
a
y
to
ch
an
g
e
V
th
in
cir
cu
it
lev
e
l
is
th
r
o
u
g
h
ch
a
n
g
i
n
g
t
h
e
V
BB
w
h
ic
h
is
k
n
o
w
n
b
y
b
o
d
y
b
ias
v
o
ltag
e
s
ca
li
n
g
(
B
B
VS)
tech
n
iq
u
e
to
m
in
i
m
ize
th
e
s
tatic
p
o
w
er
d
i
s
s
ip
atio
n
i
n
m
icr
o
p
r
o
ce
s
s
o
r
s
an
d
C
MO
S
d
ev
ice
s
.
Ho
w
e
v
er
,
t
h
e
DV
S
is
a
n
o
th
er
e
f
f
icie
n
t
tech
n
iq
u
e
t
h
at
m
i
n
i
m
ize
d
y
n
a
m
ic
p
o
w
er
co
n
s
u
m
p
tio
n
o
f
p
r
o
ce
s
s
o
r
s
b
y
s
ca
li
n
g
d
o
w
n
s
u
p
p
l
y
v
o
lta
g
e
a
n
d
f
r
eq
u
en
c
y
as
w
e
ll
w
h
e
n
p
ea
k
p
er
f
o
r
m
a
n
ce
is
u
n
n
ee
d
ed
w
h
ic
h
i
s
k
n
o
w
n
b
y
d
y
n
a
m
ic
v
o
lta
g
e
s
ca
li
n
g
tec
h
n
iq
u
e
(
DVS)
.
L
o
w
er
i
n
g
t
h
e
s
u
p
p
l
y
v
o
ltag
e
an
d
f
r
eq
u
e
n
c
y
ac
co
r
d
in
g
l
y
ca
n
r
ed
u
ce
s
i
g
n
if
ica
n
t
a
m
o
u
n
t
o
f
e
n
er
g
y
.
Fig
u
r
e
2
s
h
o
w
s
p
o
w
er
s
av
i
n
g
ac
h
iev
e
m
e
n
t
s
u
s
i
n
g
DVS.
Fig
u
r
e
3
s
h
o
w
t
h
e
d
y
n
a
m
ic
an
d
s
tat
ic
p
o
w
er
a
s
f
u
n
ctio
n
s
o
f
V
DD
f
o
r
1
6
-
b
it a
d
d
er
in
3
2
n
m
C
MO
S tec
h
n
o
lo
g
y
[
1
9
]
.
Fig
u
r
e
1
.
Fo
r
w
ar
d
an
d
r
ev
er
s
e
b
o
d
y
b
ias (
FB
B
-
R
B
B
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
10
,
No
.
6
,
Dec
em
b
er
2
0
2
0
:
6
549
-
6
5
5
7
6552
Fig
u
r
e
2
.
T
h
e
DVS
p
o
w
er
s
a
v
i
n
g
s
Fig
u
r
e
3
.
D
y
n
a
m
ic
a
n
d
s
tatic
p
o
w
er
as f
u
n
c
tio
n
s
16
-
b
it a
d
d
er
in
3
2
n
m
C
MO
S t
ec
h
n
o
lo
g
y
I
n
s
p
ite
o
f
th
i
s
,
a
s
t
h
e
s
u
p
p
l
y
v
o
ltag
e
o
f
m
icr
o
p
r
o
ce
s
s
o
r
s
g
o
es
lo
w
,
t
h
e
s
tatic
p
o
w
er
is
r
ap
id
l
y
in
cr
ea
s
i
n
g
,
t
h
u
s
j
o
in
t
DVS
an
d
B
B
VS
tech
n
iq
u
e
is
a
cr
u
ci
al
ap
p
r
o
ac
h
f
o
r
p
r
o
d
u
ctiv
e
p
o
w
er
r
ed
u
ctio
n
a
n
d
te
m
p
er
atu
r
es
f
o
r
h
ig
h
p
er
f
o
r
m
an
ce
p
r
o
ce
s
s
o
r
s
.
Hen
ce
,
t
h
i
s
tech
n
iq
u
e
is
d
es
ir
ed
to
allo
w
a
m
icr
o
p
r
o
ce
s
s
o
r
co
r
e
to
d
eliv
er
o
p
tim
al
p
er
f
o
r
m
an
ce
,
lo
w
er
p
o
w
er
d
is
s
ip
ati
o
n
an
d
o
p
ti
m
al
c
lo
ck
f
r
eq
u
e
n
c
y
as
i
t
d
ep
en
d
s
o
n
s
u
p
p
l
y
a
n
d
th
r
es
h
o
ld
v
o
lta
g
es
[
2
0
]
,
2
()
.
d
d
th
DD
VV
f
KV
(
7
)
w
h
er
e
is
th
e
v
elo
cit
y
s
a
tu
r
atio
n
co
ef
ficien
t
1
2
,
an
d
K
2
is
a
tec
h
n
o
lo
g
y
s
p
ec
i
fi
c
c
o
n
s
ta
n
t.
T
h
er
ef
o
r
e,
r
ed
u
ctio
n
o
f
d
y
n
a
m
ic
p
o
w
er
co
n
s
u
m
p
tio
n
in
v
o
lv
e
s
b
y
s
ca
l
in
g
d
o
w
n
o
f
s
u
p
p
l
y
v
o
lta
g
e
w
h
ic
h
is
k
n
o
w
n
b
y
DVS,
a
n
d
r
ed
u
ctio
n
o
f
s
tatic
p
o
w
er
co
n
s
u
m
p
tio
n
in
v
o
l
v
es
b
y
i
n
cr
ea
s
in
g
o
f
t
h
r
es
h
o
ld
v
o
l
tag
e
t
h
r
o
u
g
h
s
ca
li
n
g
o
f
b
o
d
y
b
ias
v
o
ltag
e
w
h
ic
h
is
k
n
o
w
n
b
y
B
B
VS.
3.
P
ARET
O
F
RO
N
T
(
P
F
)
AND
P
ARTI
CL
E
SWA
RM
O
P
T
I
M
I
Z
AT
I
O
N
(
P
SO
)
AL
G
O
RIT
H
M
S
Mu
lti
-
o
b
j
ec
tiv
e
o
p
ti
m
iza
tio
n
alg
o
r
ith
m
s
ar
e
d
ea
li
n
g
w
it
h
p
r
o
b
lem
s
o
f
m
u
l
tip
le
o
b
j
ec
tiv
es
o
r
o
f
te
n
co
n
tr
ad
icto
r
y
cr
i
ter
ia
to
b
e
o
p
ti
m
ized
s
i
m
u
lta
n
eo
u
s
l
y
.
W
h
er
ea
s
,
f
o
r
p
r
o
b
lem
s
i
n
cl
u
d
in
g
o
n
l
y
o
n
e
o
b
j
ec
tiv
e,
th
e
o
p
ti
m
u
m
s
et
i
n
d
e
m
a
n
d
w
ill
clea
r
l
y
b
e
id
en
t
if
y
,
it
is
al
s
o
n
ee
d
to
f
o
r
m
alize
f
o
r
m
u
lti
-
o
b
j
ec
tiv
e
o
p
tim
izatio
n
p
r
o
b
le
m
s
.
I
n
f
a
ct,
f
o
r
a
p
r
o
b
lem
w
it
h
t
w
o
o
r
m
o
r
e
co
n
tr
ad
icto
r
y
o
b
j
e
ctiv
es
,
t
h
e
o
p
ti
m
al
s
o
lu
tio
n
s
w
ill
b
e
a
s
et
o
f
p
o
i
n
ts
co
r
r
esp
o
n
d
in
g
to
th
e
b
est
p
o
s
s
ib
le
co
m
p
r
o
m
is
e
s
th
at
s
o
lv
e
th
e
p
r
o
b
lem
.
Mu
lti o
b
j
ec
tiv
e
o
p
ti
m
izatio
n
p
r
o
b
lem
m
at
h
e
m
at
icall
y
ca
n
b
e
g
iv
e
n
b
y
[
2
1
]
.
1
1
2
2
1
2
12
(
,
x
,
..
.,
)
1
(
,
x
,
..
.,
)
Min
f
(
)
w
it
h
1
..
:
(
)
0
,
(
)
0
,
(
,
x
,
..
.,
)
m
m
xc
nm
f
x
x
m
f
x
x
xn
c
x
g
x
h
x
a
x
b
f
x
x
(
8
)
w
h
er
e
f
(
x
)
is
th
e
r
elate
d
o
b
j
ec
tiv
e
f
u
n
ctio
n
.
x
is
th
e
r
ela
ted
d
ec
is
io
n
v
ar
iab
le,
n
is
n
u
m
b
er
o
f
o
b
j
ec
tiv
e
f
u
n
ctio
n
s
,
a
n
d
m
n
u
m
b
er
o
f
d
ec
is
io
n
v
ar
iab
les.
T
y
p
icall
y
,
i
t
is
u
n
w
o
r
k
ab
le
to
f
i
n
d
all
d
e
cisi
o
n
v
ar
iab
les
(
x
)
w
h
ic
h
m
i
n
i
m
izi
n
g
all
o
b
j
ec
tiv
e
f
u
n
ctio
n
s
f
1
(
x
)
,
f
2
(
x
)
.
.
.
,
f
n
(
x
)
.
T
h
e
P
F
alg
o
r
ith
m
w
a
s
in
itia
te
d
b
y
Go
ld
b
er
g
in
1
9
8
9
[
2
2
]
,
it
u
s
es
th
e
co
n
ce
p
t
o
f
d
o
m
i
n
an
c
e
to
s
elec
t
o
p
tim
a
l
s
o
l
u
tio
n
s
t
h
at
b
r
in
g
t
h
e
p
o
p
u
latio
n
to
w
ar
d
s
a
s
et
o
f
s
o
l
u
tio
n
s
w
h
ic
h
i
s
ca
lled
P
ar
eto
o
p
tim
al
s
et
(
P
OS)
.
T
h
e
m
et
h
o
d
h
a
s
p
r
o
v
e
n
to
b
e
t
h
e
m
o
s
t
ef
f
ec
ti
v
e.
No
w
ad
a
y
s
,
t
h
e
m
aj
o
r
it
y
o
f
alg
o
r
ith
m
s
u
s
e
a
P
ar
eto
ap
p
r
o
ac
h
to
d
ea
l
w
it
h
m
u
lti
-
o
b
j
ec
tiv
e
p
r
o
b
lem
s
to
d
eter
m
i
n
e
a
b
r
ief
co
m
p
r
o
m
i
s
e
s
et
o
f
s
o
lu
tio
n
r
e
s
u
lt
s
t
h
at
in
v
o
l
v
es
a
tr
ad
eo
f
f
b
et
w
ee
n
t
h
e
o
b
j
ec
tiv
es.
Fo
r
ea
ch
ele
m
e
n
t
i
n
P
O
S,
n
o
n
e
o
f
t
h
e
o
b
j
ec
tiv
e
f
u
n
ctio
n
s
ca
n
b
e
f
u
r
t
h
er
in
cr
ea
s
ed
w
i
th
o
u
t a
d
ec
r
ea
s
e
o
f
s
o
m
e
o
f
t
h
e
r
e
m
a
in
i
n
g
o
b
j
ec
tiv
e.
Ma
in
l
y
,
th
e
P
ar
eto
o
p
tim
al
s
o
lu
tio
n
s
ar
e
k
n
o
w
n
as
t
h
e
P
ar
eto
f
r
o
n
t
(
P
F)
in
th
e
o
b
j
ec
tiv
e
s
p
ac
e
an
d
th
e
P
ar
eto
o
p
tim
al
s
et
(
P
OS)
in
th
e
d
ec
is
io
n
s
p
ac
e.
T
h
er
ef
o
r
e,
an
y
s
o
lu
tio
n
in
t
h
i
s
s
et
ca
r
r
ies
s
a
m
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2
0
8
8
-
8708
Mu
lti
-
o
b
jective
P
F
a
n
d
P
S
O
a
lg
o
r
ith
ms fo
r
p
o
w
er
.
..
(
Dia
r
y
R
.
S
u
la
ima
n
)
6553
s
ig
n
i
f
ica
n
ce
an
d
is
a
g
r
ea
t c
o
m
p
r
o
m
is
e
a
m
o
n
g
th
e
tr
ad
eo
f
f
o
b
j
ec
tiv
es.
Fi
g
u
r
e
4
s
h
o
w
s
d
e
cisi
o
n
v
ar
iab
le
s
p
ac
e
w
it
h
t
w
o
o
b
j
ec
tiv
e
f
u
n
ctio
n
s
a
n
d
P
ar
eto
f
r
o
n
t
(
P
F)
.
Op
ti
m
al
p
o
w
er
d
is
s
ip
atio
n
i
s
a
m
u
lt
i
-
cr
iter
ia
o
p
ti
m
izatio
n
p
r
o
b
lem
;
co
m
m
o
n
l
y
th
e
p
r
o
b
lem
is
co
n
v
er
ted
in
to
a
p
ar
am
etr
ic
au
x
i
l
iar
y
s
i
n
g
le
o
b
j
ec
tiv
e
p
r
o
b
lem
,
its
co
n
ce
p
tu
al
s
o
l
u
tio
n
p
r
o
v
i
d
es
a
P
a
r
eto
-
o
p
tim
al
p
o
in
t
b
y
d
eter
m
in
in
g
o
p
ti
m
al
b
o
d
y
b
ias,
th
r
esh
o
ld
,
an
d
s
u
p
p
l
y
v
o
ltag
e
s
(
V
BB
/V
th
-
V
DD
)
th
at
en
s
u
r
e
s
o
p
ti
m
al
p
o
w
er
d
i
s
s
ip
atio
n
r
ed
u
ct
io
n
.
Fig
u
r
e
4
.
Dec
is
io
n
v
ar
iab
le
s
p
ac
e
w
i
t
h
t
w
o
o
b
j
ec
tiv
e
f
u
n
ctio
n
s
a
n
d
P
ar
eto
f
r
o
n
t (
P
F)
R
ed
u
ci
n
g
co
m
p
u
tatio
n
al
ti
m
e
d
elay
as
w
ell
as
t
h
e
s
ea
r
ch
i
n
g
zo
n
e,
th
e
P
F
d
o
m
i
n
a
n
ce
f
u
n
d
a
m
e
n
tal
s
ar
e
u
tili
ze
d
alo
n
g
w
it
h
P
SO
ap
p
r
o
ac
h
p
r
in
cip
les.
P
OS
is
ap
p
lied
f
o
r
id
en
tify
i
n
g
all
f
e
asib
le
s
et
s
o
f
j
o
in
t
V
th
/V
BB
-
V
DD
w
h
ic
h
b
r
in
g
m
u
l
titu
d
e
o
f
p
o
s
s
ib
le
r
ig
h
t
c
h
o
ices.
Fo
r
th
i
s
j
o
in
t
p
r
o
ce
s
s
o
f
d
ec
is
io
n
v
ar
iab
les
a
n
d
d
o
m
i
n
a
n
ce
s
et
s
;
n
o
n
e
o
f
t
h
e
V
th
/V
BB
a
n
d
V
DD
ca
n
i
m
p
r
o
v
e
u
n
le
s
s
lo
w
er
i
n
g
s
o
m
e
o
t
h
er
o
b
j
ec
tiv
e
v
ar
iab
le
v
alu
e
s
.
T
h
e
P
F
o
f
n
o
n
-
d
o
m
i
n
ated
s
et
o
f
s
o
lu
tio
n
s
is
esti
m
a
ted
b
ased
o
n
a
f
r
ee
r
u
n
n
i
n
g
p
r
o
ce
s
s
in
th
e
d
esi
g
n
zo
n
e
th
at
co
n
tai
n
i
n
g
all
j
o
in
t
m
i
n
i
m
izatio
n
s
ets,
an
d
th
e
n
m
in
i
m
izatio
n
to
a
r
estrictiv
e
s
et
is
ap
p
lied
to
r
estrict
th
e
s
ea
r
c
h
s
p
ac
e
w
it
h
n
e
w
r
e
s
tr
icted
co
n
s
tr
ai
n
ts
a
n
d
li
m
ita
tio
n
s
.
Ob
tai
n
in
g
w
ei
g
h
ti
n
g
f
a
cto
r
s
ar
e
b
asicall
y
d
ep
en
d
in
g
o
n
t
h
e
m
u
l
tio
b
j
ec
t
iv
e
P
F
i
n
o
r
d
er
to
n
o
r
m
alize
p
o
w
er
d
is
s
ip
atio
n
an
d
te
m
p
er
atu
r
es
a
s
o
b
j
ec
tiv
e
f
u
n
ctio
n
s
w
it
h
a
v
er
y
g
o
o
d
in
f
l
u
en
ce
to
co
n
v
er
g
e
n
ce
[
2
3
]
.
P
SO
alg
o
r
ith
m
w
a
s
p
r
o
p
o
s
ed
b
y
R
u
s
s
e
l
E
b
er
h
ar
t
(
ele
ctr
ical
en
g
i
n
ee
r
)
an
d
J
a
m
es
Ken
n
ed
y
(
s
o
cio
-
p
s
y
c
h
o
lo
g
i
s
t)
in
1
9
9
5
[
2
4
]
.
P
SO
is
a
s
to
ch
asti
c
alg
o
r
ith
m
i
n
o
p
ti
m
izatio
n
m
et
h
o
d
s
th
at
m
o
d
eled
b
y
a
m
ath
e
m
atica
l
eq
u
atio
n
to
g
u
id
e
th
e
"
p
ar
ticles
"
d
u
r
in
g
th
e
d
is
p
lace
m
e
n
t
p
r
o
ce
s
s
.
T
h
e
m
o
tio
n
o
f
a
p
ar
ticle
is
af
f
ec
ted
b
y
th
r
ee
co
m
p
o
n
e
n
t
s
:
s
o
cial
co
m
p
o
n
e
n
t,
i
n
er
tia
co
m
p
o
n
e
n
t,
a
n
d
co
g
n
i
tiv
e
co
m
p
o
n
en
t.
P
SO
h
as
ac
cu
r
ate
ad
v
an
ta
g
e
o
f
p
o
w
er
d
is
s
ip
atio
n
p
r
o
b
lem
s
i
n
m
icr
o
p
r
o
ce
s
s
o
r
s
an
d
VL
S
I
d
esig
n
s
,
w
it
h
o
u
t
u
s
er
-
d
ef
i
n
ed
m
o
d
i
f
icatio
n
o
f
th
e
s
tr
u
ct
u
r
e
o
f
t
h
e
alg
o
r
it
h
m
.
A
s
w
ar
m
o
f
p
ar
ticles
ar
e
f
e
asib
le
s
o
lu
t
io
n
s
to
o
p
tim
izatio
n
p
r
o
b
le
m
o
v
er
th
e
r
esear
ch
s
p
ac
e
to
d
eter
m
i
n
e
g
lo
b
al
o
p
ti
m
u
m
[
2
5
,
2
6
]
.
T
h
e
p
ar
ticle
d
is
p
lace
m
e
n
t stra
te
g
y
i
s
ill
u
s
tr
ated
in
Fig
u
r
e
5
.
Fig
u
r
e
5
.
T
h
e
p
ar
ticles d
is
p
lac
e
m
en
t
I
n
th
e
r
esear
c
h
s
p
ac
e
o
f
d
i
m
e
n
s
io
n
D,
th
e
p
ar
ticle
i
n
th
e
s
w
ar
m
is
m
o
d
eled
b
y
i
ts
p
o
s
it
io
n
v
ec
to
r
→
=
(
1
,
2
,
…
,
)
an
d
b
y
its
s
p
ee
d
v
ec
to
r
→
=
(
1
,
2
,
…
,
)
.
T
h
e
q
u
ality
o
f
t
h
e
p
o
s
itio
n
i
s
d
eter
m
in
ed
b
y
t
h
e
v
alu
e
o
f
t
h
e
o
b
j
ec
tiv
e
f
u
n
ctio
n
at
t
h
is
p
o
in
t.
T
h
is
p
ar
ticle
k
ee
p
s
i
n
m
e
m
o
r
y
t
h
e
b
est
p
o
s
itio
n
th
r
o
u
g
h
t
h
at
h
as
alr
ea
d
y
p
ass
ed
,
ex
p
r
ess
ed
b
y
→
=
(
1
,
2
,
…
,
)
.
T
h
e
b
est
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
10
,
No
.
6
,
Dec
em
b
er
2
0
2
0
:
6
549
-
6
5
5
7
6554
p
o
s
itio
n
r
ea
ch
ed
b
y
th
e
s
w
a
r
m
p
ar
ticles
i
s
ex
p
r
es
s
ed
b
y
→
=
(
1
,
2
,
…
,
)
as
th
e
g
lo
b
al
b
est
P
SO,
w
h
er
e
a
l
l
p
ar
ticles
i
n
t
h
e
s
w
ar
m
ar
e
c
o
n
s
id
er
ed
to
b
e
clo
s
e
to
p
ar
ticle
i
.
P
ar
ticle
m
o
v
e
s
to
w
ar
d
s
a
b
es
t
s
o
l
u
tio
n
ca
l
led
g
lo
b
al
s
o
lu
tio
n
(
g
b
est)
in
a
s
ea
r
c
h
zo
n
e
b
y
u
p
g
r
a
d
in
g
its
v
elo
cit
y
;
1
1
[
]
2
[
]
,
1
,
2
,
...,
12
.
,
,
,
,
,
,,
tt
t
t
t
t
t
t
pb
e
s
t
pb
e
s
t
w
c
c
j
D
v
v
x
x
rr
i
j
i
j
i
j
i
j
i
j
i
j
i
j
i
j
an
d
p
o
s
itio
n
;
1
,
1
,
2
,
.
.
.
,
,,
tt
x
j
D
xv
i
j
i
j
,
ω
is
co
ef
f
icien
t
o
f
i
n
er
tia
(
co
n
s
ta
n
t)
;
c1
an
d
c2
ar
e
co
n
s
ta
n
ts
,
k
n
o
w
n
b
y
ac
ce
ler
atio
n
co
e
f
f
icien
t
s
;
r
1
an
d
r
2
ar
e
r
an
d
o
m
n
u
m
b
er
s
d
r
a
w
n
u
n
if
o
r
m
l
y
i
n
[
0
,
1
]
,
at
ea
ch
iter
atio
n
t
an
d
f
o
r
ea
ch
d
i
m
e
n
s
io
n
j
.
On
ce
th
e
p
ar
ticle
s
m
o
v
ed
,
th
e
n
e
w
p
o
s
it
io
n
s
ar
e
ev
alu
a
ted
an
d
th
e
t
w
o
v
ec
to
r
s
o
f
P
b
est
an
d
g
b
est
w
ill
u
p
d
at
e
ac
co
r
d
in
g
l
y
,
n
is
th
e
n
u
m
b
er
o
f
p
ar
ticle
s
in
t
h
e
s
w
ar
m
[
2
7
,
2
8
]
.
(
t)
(
(
t)
(
x
(
t
1
)
)
(
t
1
)
(
t
1
)
(
p
b
e
s
t(
t)
(
x
(
t
1
)
)
P
b
e
s
t
if
P
b
e
s
t
f
P
b
e
s
t
P
b
e
s
t
if
f
(
9
)
12
m
in
,
,
...,
......
,
1
i
gbe
st
Pbe
st
Pbe
st
Pbe
st
i
n
(
1
0
)
T
h
er
e
ar
e
m
a
n
y
p
r
o
b
le
m
s
in
o
p
tim
a
l
co
m
b
in
atio
n
s
ets
t
h
at
p
r
o
v
id
e
c
o
m
p
le
x
ities
a
n
d
d
if
f
ic
u
lties
i
n
th
e
e
v
alu
a
tio
n
o
f
o
p
ti
m
al
v
al
u
e
p
o
in
t
s
.
Ob
j
ec
tiv
e
f
u
n
ctio
n
s
ar
e
co
n
f
licti
n
g
to
ea
ch
o
t
h
e
r
,
n
o
t
j
u
s
t
a
s
in
g
l
e
o
p
tim
u
m
s
o
lu
tio
n
b
u
t
also
s
et
s
o
f
o
p
ti
m
al
s
o
lu
tio
n
s
ar
e
a
v
ai
lab
le.
T
h
e
o
b
j
ec
tiv
e
f
u
n
ctio
n
o
f
m
in
i
m
u
m
p
o
w
er
d
is
s
ip
atio
n
(
P
opt
)
co
n
s
titu
te
a
m
u
lti
d
i
m
en
s
io
n
al
s
p
ac
e
o
f
b
o
d
y
a
n
d
s
u
p
p
l
y
v
o
lta
g
e
s
(
V
BB
/V
th
,
V
DD
)
,
an
d
o
th
er
s
tate
an
d
tec
h
n
o
lo
g
y
p
ar
a
m
e
ter
s
to
b
e
tak
e
i
n
to
co
n
s
id
er
atio
n
as
f
itt
in
g
p
ar
a
m
eter
s
.
Fo
r
ea
ch
s
o
lu
tio
n
,
te
m
p
er
atu
r
e,
V
BB
/V
th
,
V
dd
,
lo
ad
ca
p
ac
itan
ce
(
C
)
,
an
d
f
itt
in
g
p
ar
am
eter
s
ar
e
th
e
d
ec
is
io
n
v
a
r
iab
les,
r
ath
er
th
a
n
co
n
s
ta
n
t
t
h
at
ta
k
es
i
n
to
co
n
s
id
er
atio
n
.
W
e
w
i
ll
tr
y
to
d
eter
m
in
e
t
h
e
f
ea
s
ib
le
r
eg
io
n
o
f
P
o
pt
as
a
P
ar
eto
o
p
tim
al
s
et
o
r
P
ar
eto
o
p
tim
al
s
o
l
u
tio
n
s
.
Fo
r
all
b
o
d
y
b
ias
a
n
d
s
u
p
p
l
y
v
o
ltag
e
s
,
th
e
P
OS
alg
o
r
it
h
m
c
an
r
etr
iev
e
t
h
o
u
s
a
n
d
s
o
f
p
o
s
s
i
b
ilit
ies
to
d
eter
m
in
e
all
V
th
-
V
DD
j
o
in
t
s
ets,
n
o
n
e
o
f
th
e
V
BB
/V
th
a
n
d
V
DD
v
alu
e
s
ca
n
b
e
im
p
r
o
v
e
w
it
h
o
u
t
m
o
d
if
y
i
n
g
s
o
m
e
v
alu
e
s
o
f
o
th
er
o
b
j
ec
tiv
e
v
ar
i
ab
les.
Fo
r
th
e
ev
al
u
atio
n
o
f
P
OS
o
f
n
o
n
-
d
o
m
in
ated
s
o
l
u
tio
n
s
,
a
co
m
p
le
x
co
d
e
ar
r
an
g
e
m
en
t
w
as
u
s
ed
.
T
h
e
P
OS
o
f
n
o
n
-
d
o
m
i
n
ated
s
o
l
u
ti
o
n
s
i
s
e
s
ti
m
ates
th
r
o
u
g
h
a
s
t
o
ch
asti
c
e
v
al
u
atio
n
me
t
h
o
d
o
f
th
e
d
esi
g
n
f
o
r
d
if
f
e
r
en
t te
m
p
er
at
u
r
e
lev
el
s
an
d
d
if
f
er
en
t b
o
d
y
b
ias an
d
s
u
p
p
l
y
v
o
ltag
es.
I
t
is
o
b
v
io
u
s
t
h
at,
t
h
e
p
r
o
ce
d
u
r
e
is
th
e
o
p
ti
m
izatio
n
o
f
co
n
f
li
ctin
g
v
ar
iab
les
a
n
d
o
b
j
ec
tiv
es.
W
e
w
er
e
ab
le
to
u
s
e
o
n
e
o
b
j
ec
tiv
e
f
u
n
c
tio
n
at
a
ti
m
e
o
r
lin
k
i
n
g
all
o
b
j
ec
tiv
es
w
it
h
a
co
m
m
o
n
f
ea
t
u
r
e
w
eig
h
t
.
So
,
n
o
n
e
o
f
s
ets
is
d
ef
i
n
ite
l
y
p
r
ef
er
ab
le
as
co
m
p
ar
ed
to
o
th
er
s
ets.
W
e
ca
ll
th
is
t
h
e
“n
o
n
-
d
o
m
i
n
a
ted
”
s
et
o
f
s
o
l
u
tio
n
s
m
ea
n
s
th
at,
n
o
n
e
o
f
th
e
s
o
l
u
tio
n
s
s
ets
ar
e
d
o
m
i
n
ated
.
A
ll
P
ar
eto
-
o
p
ti
m
al
s
o
l
u
tio
n
s
ar
e
n
o
n
-
d
o
m
i
n
ated
.
T
h
u
s
,
it
is
cr
u
cial
to
attr
ib
u
te
th
e
s
o
lu
tio
n
s
as
clo
s
e
as
p
o
s
s
ib
le
to
t
h
e
P
OS
as
f
ar
as
p
o
s
s
ib
le
u
s
i
n
g
a
co
m
b
in
atio
n
o
f
n
u
m
er
ical
w
ei
g
h
ts
f
o
r
t
h
e
o
b
j
ec
tiv
es
[
2
9
]
.
T
h
er
e
ar
e
d
if
f
er
e
n
t
m
eth
o
d
s
u
s
ed
i
n
p
r
ac
tice,
b
u
t
o
n
e
is
to
u
s
e
a
P
SO
a
lg
o
r
ith
m
t
o
s
p
ec
i
f
y
p
o
i
n
ts
alo
n
g
t
h
e
P
ar
eto
o
p
tim
al
s
o
lu
tio
n
o
v
er
d
if
f
er
e
n
t
s
e
v
er
al
iter
atio
n
s
,
th
e
n
r
an
k
an
d
ev
a
lu
ate
th
e
q
u
al
it
y
o
f
t
h
e
tr
ad
e
-
o
f
f
s
b
ased
o
n
th
e
p
ar
ticu
lar
ap
p
licatio
n
b
ein
g
m
o
d
eled
.
T
h
er
ef
o
r
e,
P
SO
is
ca
lled
f
o
r
to
d
eter
m
i
n
e
th
e
b
est
v
ar
iab
les
v
alu
e
i
n
P
OS
th
at
m
ee
t
t
h
e
o
p
ti
m
al
p
o
w
er
d
is
s
ip
atio
n
.
T
h
e
p
r
esen
ted
P
F
an
d
P
SO
alg
o
r
ith
m
s
ar
e
cr
u
cial
f
o
r
d
eter
m
in
i
n
g
o
p
ti
m
al
a
m
o
u
n
t
o
f
p
o
w
er
d
is
s
ip
atio
n
m
i
n
i
m
izatio
n
; it
m
ea
n
s
t
h
at
t
h
e
p
r
o
b
le
m
is
m
i
n
i
m
izatio
n
.
Min
i
m
ize
th
e
o
b
j
ec
tiv
e
f
u
n
ctio
n
o
f
m
u
lti
v
ar
iab
les
w
it
h
b
o
u
n
d
co
n
s
tr
ai
n
ts
r
eq
u
ir
es;
d
e
f
in
in
g
t
h
e
o
b
j
ec
tiv
e
f
u
n
ct
io
n
s
,
s
etti
n
g
b
o
u
n
d
s
o
n
t
h
e
v
ar
iab
le
s
,
ca
llin
g
p
ar
ticle
s
w
ar
m
to
m
in
i
m
ize
t
h
e
f
u
n
ct
io
n
,
o
r
u
s
e
th
e
p
r
iv
ate
co
d
e,
Fin
all
y
,
o
p
ti
m
izat
io
n
en
d
ed
:
r
elativ
e
ch
an
g
e
i
n
t
h
e
o
b
j
ec
tiv
e
v
al
u
e.
Ho
w
e
v
er
,
an
d
i
n
s
tead
o
f
al
l,
P
ar
eto
XL
S
f
ile
s
ar
e
co
n
s
id
er
ed
as
th
e
n
e
w
s
ea
r
ch
s
p
ac
e
b
o
u
n
d
.
A
ll
i
n
d
iv
id
u
als
s
ea
r
ch
s
p
ac
e
o
f
P
SO
f
lies
h
a
s
a
v
elo
cit
y
t
h
at
d
y
n
a
m
ica
ll
y
ad
j
u
s
ted
.
Fo
r
all
s
tep
s
,
th
e
P
SO
al
g
o
r
ith
m
c
h
a
n
g
e
s
v
elo
cit
y
o
f
ea
ch
p
ar
ticle
to
w
ar
d
its
p
o
s
itio
n
-
b
est
(
p
b
est
)
a
n
d
g
lo
b
al
-
b
est
(
g
b
est
)
lo
ca
tio
n
s
.
T
h
e
ac
ce
ler
atio
n
is
w
ei
g
h
ted
r
an
d
o
m
l
y
w
it
h
m
u
lti
p
le
g
en
er
ated
r
an
d
o
m
n
u
m
b
er
s
to
w
ar
d
p
b
est
an
d
g
b
est
lo
ca
tio
n
s
.
T
h
e
m
o
d
if
ie
d
p
o
s
itio
n
an
d
v
elo
cit
y
o
f
ea
ch
in
d
i
v
id
u
al
p
ar
ticle
ca
n
b
e
ca
lcu
late
u
s
i
n
g
th
e
cu
r
r
en
t v
el
o
cit
y
an
d
t
h
e
d
is
tan
ce
f
r
o
m
p
b
est
(
cu
r
r
en
t p
o
s
itio
n
)
to
g
b
est
(
b
est p
o
s
itio
n
)
[
3
0
]
.
T
h
e
p
o
w
er
d
i
s
s
ip
atio
n
f
u
n
ctio
n
ta
k
es
an
ar
r
a
y
o
f
i
n
p
u
t
s
a
n
d
p
r
o
d
u
ce
s
a
s
i
n
g
le
o
u
tp
u
t.
T
h
e
o
b
j
ec
tiv
e
is
i
n
f
in
d
i
n
g
w
h
at
i
n
p
u
t
r
esu
lt
s
i
n
t
h
e
lo
w
es
t
p
o
s
s
ib
le
o
u
tp
u
t
f
o
r
t
h
e
p
o
w
er
d
i
s
s
ip
atio
n
f
u
n
ctio
n
.
Sin
ce
,
t
h
e
f
u
n
c
tio
n
w
as
n
’
t
d
if
f
er
en
tiab
le
an
d
t
h
e
r
an
g
e
o
f
in
p
u
ts
w
er
e
q
u
i
te
s
m
a
ll,
P
SO
co
u
ld
j
u
s
t
s
ea
r
ch
th
e
en
t
ir
e
in
p
u
t sp
ac
e
to
f
in
d
t
h
e
b
est o
u
tp
u
t.
T
h
e
r
esu
lts
w
er
e
ac
h
ie
v
ed
in
a
C
P
U
en
v
ir
o
n
m
e
n
t,
i.e
.
j
u
s
t
o
n
e
s
o
lu
tio
n
w
it
h
i
n
th
e
s
ea
r
c
h
s
p
ac
e
w
as
ev
alu
a
ted
at
p
ar
ticu
lar
m
o
m
e
n
t.
T
h
e
P
SO
o
p
ti
m
iza
tio
n
r
e
s
u
lt
s
co
n
f
ir
m
t
h
e
m
icr
o
p
r
o
ce
s
s
o
r
’
s
p
er
f
o
r
m
a
n
ce
g
ain
.
I
t
g
i
v
es
co
m
b
in
at
io
n
s
o
f
p
ar
a
m
eter
to
en
h
an
ce
th
e
w
h
o
le
o
p
ti
m
izat
io
n
p
er
f
o
r
m
a
n
ce
.
I
ts
co
n
ce
p
t
u
a
l
s
i
m
p
lic
it
y
p
r
o
d
u
ce
s
ap
p
l
y
i
n
g
t
h
is
m
et
h
o
d
a
s
tr
aig
h
tf
o
r
w
ar
d
s
tu
d
y
p
lan
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2
0
8
8
-
8708
Mu
lti
-
o
b
jective
P
F
a
n
d
P
S
O
a
lg
o
r
ith
ms fo
r
p
o
w
er
.
..
(
Dia
r
y
R
.
S
u
la
ima
n
)
6555
4.
RE
SU
L
T
S
AND
D
I
SCU
SS
I
O
N
I
n
o
r
d
er
to
in
q
u
ir
e
f
o
r
c
h
ec
k
in
g
o
p
er
atio
n
,
f
u
n
ctio
n
alit
y
,
an
d
i
n
teg
r
it
y
o
f
t
h
e
P
F
-
P
SO
ap
p
r
o
ac
h
;
th
e
d
ev
ice
u
n
d
er
test
is
b
ei
n
g
u
s
ed
f
o
r
co
r
r
ec
t
o
p
e
r
atio
n
.
T
h
e
elec
tr
ical
r
esp
o
n
s
es
ar
e
s
t
u
d
ied
to
d
eter
m
in
e
th
e
d
esi
g
n
p
r
o
d
u
cti
v
it
y
i
n
p
o
w
er
d
is
s
ip
atio
n
r
ed
u
ctio
n
o
f
th
e
c
h
ip
.
Usi
n
g
2
2
n
m
lith
o
g
r
ap
h
y
m
ee
t
s
p
ec
if
icatio
n
o
f
t
h
e
n
e
w
p
r
o
ce
s
s
o
r
f
a
m
i
lies
a
n
d
co
m
p
atib
le
w
it
h
t
h
e
last
SP
I
C
E
s
i
m
u
latio
n
s
o
f
t
w
ar
e
.
Sp
ice
-
XVI
I
an
d
OR
C
A
D
1
7
.
2
ar
e
u
s
ed
f
o
r
th
e
s
p
ec
if
icatio
n
o
f
I
n
tel®
C
o
r
e
™
i7
P
r
o
ce
s
s
o
r
s
.
T
h
e
tem
p
er
at
u
r
es
r
an
g
es
(
3
0
-
7
0
)
o
C
o
f
th
e
s
e
p
r
o
ce
s
s
o
r
s
ar
e
class
i
f
ied
i
n
to
:
id
le
te
m
p
er
at
u
r
e,
n
o
r
m
a
l
te
m
p
er
atu
r
e
a
n
d
m
ax
i
m
u
m
t
e
m
p
er
atu
r
es.
W
h
ile
t
h
e
v
o
lt
ag
e
id
en
t
if
icatio
n
r
a
n
g
e
is
0
.
8
0
0
V
-
1
.
3
7
5
V
an
d
th
e
co
r
e
v
o
ltag
e
(
V
core
)
r
an
g
e
is
0
.
9
3
0
-
1
.
2
0
5
.
T
h
e
m
o
d
el
is
ca
lc
u
lated
a
n
d
r
es
u
lt
s
ar
e
r
ec
o
r
d
ed
,
f
o
r
ea
ch
d
eg
r
ee
o
f
te
m
p
er
at
u
r
es
a
n
d
t
y
p
ica
l
w
o
r
k
lo
ad
s
f
o
r
d
if
f
er
en
t
b
en
ch
m
ar
k
p
r
o
g
r
a
m
s
li
k
e
T
Z
0
0
,
T
Z
0
1
,
T
PIN
5
,
T
P
I
N6
,
an
d
T
P
I
N3
.
B
ased
o
n
th
ese
ev
al
u
atio
n
s
a
t
y
p
ical
1
o
C
d
eg
r
ee
is
s
e
lecte
d
as
th
e
te
m
p
er
at
u
r
e
w
id
th
f
o
r
all
p
r
o
ce
s
s
o
r
’
s
o
p
er
atio
n
m
o
d
es.
I
n
o
r
d
er
to
co
n
f
ir
m
th
e
r
e
s
u
l
ts
f
in
d
i
n
g
o
p
ti
m
al
V
th
-
V
DD
s
ets;
P
ar
eto
o
p
tim
a
l
s
o
l
u
tio
n
(
P
OS)
an
d
P
ar
ticle
S
w
ar
m
Op
ti
m
izat
io
n
(
P
SO)
alg
o
r
ith
m
ar
e
u
s
ed
f
o
r
o
p
tim
a
l
lev
el
o
f
p
o
w
er
d
is
s
ip
atio
n
m
i
n
i
m
izatio
n
f
o
r
m
u
lti
-
le
v
el
o
f
te
m
p
er
atu
r
e
an
d
w
o
r
k
lo
ad
co
n
d
itio
n
s
.
First,
T
h
e
P
F
o
f
n
o
n
-
d
o
m
i
n
ated
s
o
l
u
tio
n
s
i
s
m
ea
s
u
r
ed
f
o
r
d
if
f
er
en
t
b
o
d
y
a
n
d
s
u
p
p
l
y
v
o
ltag
e
s
,
T
h
en
P
S
O
d
eter
m
i
n
ed
th
e
o
p
ti
m
al
v
ar
i
ab
le
v
alu
e
s
.
T
ab
le
1
s
h
o
w
s
o
p
ti
m
al
s
i
m
u
latio
n
o
f
P
F
-
P
SO a
l
g
o
r
ith
m
r
es
u
lts
.
Fi
g
u
r
e
6
s
h
o
w
s
t
h
e
p
o
w
er
m
i
n
i
m
iz
atio
n
p
er
ce
n
tag
e
s
.
T
ab
le
1
.
T
h
e
o
p
tim
al
P
F
-
P
SO
r
esu
lt
s
T
e
mp
(
o
C)
V
BB
N
-
o
pt
(V)
V
DD
-
o
pt
(V)
V
BB
P
-
o
pt
(V)
V
th
-
o
pt
(V)
f
c
l
k
-
o
pt
(
g
H
z
)
P
o
pt
(
P
W
)
M
i
n
i
m
i
z
e
d
P
o
w
e
r
(
%)
30
-
31
-
0
.
2
5
4
0
.
9
0
4
1
.
1
5
8
0
.
5
5
3
2
.
1
2
4
1
.
2
4
8
2
.
9
5
4
32
-
33
-
0
.
2
8
7
0
.
9
1
4
1
.
2
0
1
0
.
5
6
7
2
.
1
3
3
1
.
3
1
0
3
.
5
6
5
34
-
35
-
0
.
3
1
1
0
.
9
2
0
1
.
2
3
1
0
.
5
7
8
2
.
1
4
1
1
.
3
4
1
4
.
1
2
8
36
-
37
-
0
.
3
2
0
0
.
9
3
2
1
.
2
5
2
0
.
5
8
0
2
.
1
5
4
1
.
3
8
8
4
.
7
8
5
38
-
39
-
0
.
3
4
7
0
.
9
3
9
1
.
2
8
6
0
.
5
8
4
2
.
1
6
2
1
.
4
1
8
5
.
3
1
8
40
-
41
-
0
.
3
5
8
0
.
9
4
3
1
.
3
0
1
0
.
5
8
6
2
.
1
8
6
1
.
4
9
0
6
.
2
3
2
42
-
43
-
0
.
3
6
5
0
.
9
4
7
1
.
3
1
2
0
.
5
8
2
2
.
2
4
8
1
.
5
6
3
6
.
8
3
1
44
-
45
-
0
.
3
7
5
0
.
9
5
1
1
.
3
2
6
0
.
5
9
1
2
.
2
5
6
1
.
6
1
1
7
.
2
3
8
46
-
47
-
0
.
3
8
1
0
.
9
5
9
1
.
3
4
0
0
.
6
0
3
2
.
2
8
6
1
.
6
8
9
7
.
6
7
1
48
-
49
-
0
.
3
8
8
0
.
9
6
1
1
.
3
4
9
0
.
6
0
9
2
.
3
1
2
1
.
6
9
9
8
.
2
6
3
50
-
51
-
0
.
3
9
5
0
.
9
6
8
1
.
3
6
3
0
.
6
1
2
2
.
3
2
3
1
.
7
7
1
8
.
9
5
2
52
-
53
-
0
.
4
1
6
0
.
9
7
3
1
.
3
8
9
0
.
6
2
7
2
.
3
6
7
1
.
7
8
5
9
.
4
5
6
54
-
55
-
0
.
4
2
2
0
.
9
9
7
1
.
4
1
9
0
.
6
3
1
2
.
3
7
5
1
.
8
1
4
1
0
.
1
2
3
56
-
57
-
0
.
4
3
3
1
.
0
8
5
1
.
5
1
8
0
.
6
3
9
2
.
3
8
1
1
.
8
5
6
1
0
.
7
7
8
58
-
59
-
0
.
4
4
7
1
.
1
1
0
1
.
5
5
7
0
.
6
4
8
2
.
3
8
9
1
.
9
2
5
1
1
.
1
5
7
60
-
61
-
0
.
4
5
8
1
.
1
2
8
1
.
5
8
6
0
.
6
5
4
2
.
4
0
1
2
.
0
4
5
1
1
.
8
5
2
62
-
63
-
0
.
4
6
9
1
.
1
3
6
1
.
6
0
5
0
.
6
5
9
2
.
4
2
1
2
.
1
3
2
1
2
.
4
0
8
64
-
65
-
0
.
4
8
3
1
.
3
9
0
1
.
8
7
3
0
.
6
6
1
2
.
4
3
3
2
.
4
5
2
1
3
.
1
4
0
66
-
67
-
0
.
5
1
1
1
.
1
4
2
1
.
6
5
3
0
.
6
6
7
2
.
4
5
4
2
.
5
4
8
1
3
.
6
7
2
68
-
69
-
0
.
5
6
7
1
.
1
6
5
1
.
7
3
2
0
.
6
7
3
2
.
4
6
1
2
.
8
5
9
1
4
.
2
3
1
70
-
71
-
0
.
5
6
7
1
.
1
8
8
1
.
7
5
5
0
.
6
8
4
2
.
4
8
3
3
.
1
1
2
1
5
.
4
7
8
Fig
u
r
e
6
.
Th
e
p
o
w
er
m
i
n
i
m
iza
tio
n
p
er
ce
n
tag
e
s
0
2
4
6
8
10
12
14
16
P
O
W
E
R
(P
W
)
TEM
P
(C)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
10
,
No
.
6
,
Dec
em
b
er
2
0
2
0
:
6
549
-
6
5
5
7
6556
I
t
is
clea
r
t
h
at,
t
h
e
p
o
w
er
co
n
s
u
m
p
tio
n
i
s
a
co
n
v
e
x
f
u
n
ctio
n
o
f
th
e
b
o
d
y
an
d
s
u
p
p
l
y
v
o
lta
g
e.
A
d
ir
ec
t
co
n
s
id
er
atio
n
to
m
i
n
i
m
ize
p
o
w
er
co
n
s
u
m
p
tio
n
o
f
a
m
icr
o
p
r
o
ce
s
s
o
r
d
ev
ice
s
u
b
j
ec
t
to
a
g
iv
en
b
o
d
y
an
d
s
u
p
p
l
y
v
o
ltag
e
co
n
s
tr
ai
n
t
as
p
r
esen
t
ed
.
A
n
i
m
p
le
m
e
n
tatio
n
an
d
o
p
tim
izatio
n
r
e
s
u
lt
s
ar
e
p
r
es
en
ted
to
estab
lis
h
th
e
i
m
p
ac
t a
n
d
u
s
ef
u
l
n
es
s
o
f
t
h
e
ap
p
r
o
ac
h
.
T
h
ese
v
alid
atio
n
r
esu
l
ts
in
d
i
ca
te
th
at,
o
u
r
ap
p
r
o
ac
h
in
jo
in
t
th
r
es
h
o
ld
an
d
s
u
p
p
l
y
v
o
ltag
e
w
a
s
su
cc
e
s
s
f
u
l
b
y
d
y
n
a
m
icall
y
t
u
n
i
n
g
th
r
es
h
o
ld
an
d
s
u
p
p
l
y
v
o
ltag
e
s
th
r
o
u
g
h
r
ev
er
s
e
b
o
d
y
-
b
ias
a
n
d
v
o
ltag
e
s
ca
lin
g
a
n
d
is
in
d
icate
d
i
ts
e
f
f
ec
tiv
e
n
ess
f
o
r
r
ed
u
ci
n
g
h
ig
h
t
e
m
p
er
atu
r
es
a
n
d
p
o
w
er
d
i
s
s
ip
atio
n
w
h
ile
k
ee
p
in
g
f
i
x
ed
s
p
ee
d
in
t
h
e
ac
ti
v
e
an
d
i
d
le
o
p
er
atio
n
al
m
o
d
es
o
f
m
icr
o
p
r
o
ce
s
s
o
r
s
,
an
d
ex
p
er
ien
cin
g
s
ig
n
i
f
ica
n
t
p
o
w
er
an
d
te
m
p
er
atu
r
e
v
ar
iatio
n
s
.
T
h
ese
r
esu
lt
s
v
er
if
y
i
n
g
th
a
t,
P
F
-
P
SO
r
es
u
lt
s
f
o
r
d
y
n
a
m
ic
o
p
er
atio
n
al
w
o
r
k
lo
ad
o
r
te
m
p
er
a
tu
r
e
ti
m
e
li
m
it
s
,
th
e
p
r
o
ce
s
s
o
r
h
av
e
a
m
i
n
i
m
u
m
a
v
er
ag
e
p
o
w
er
co
n
s
u
m
p
tio
n
w
h
e
n
a
p
p
ly
in
g
ad
ap
tiv
e
V
th
-
V
DD
a
n
d
d
ir
ec
ted
to
h
ig
h
p
er
f
o
r
m
a
n
ce
esti
m
atio
n
.
I
t
is
v
er
i
f
y
i
n
g
ad
a
p
tiv
e
j
o
in
t
V
th
-
V
DD
tec
h
n
iq
u
e
f
o
r
p
o
w
er
as
w
el
l
as
te
m
p
er
atu
r
e
a
w
ar
e
to
th
e
p
r
o
c
ess
o
r
d
esig
n
er
s
in
t
h
eir
v
er
i
f
ic
atio
n
ef
f
o
r
ts
d
u
e
to
th
e
w
o
r
k
lo
ad
’
s
p
ar
allel
n
at
u
r
e
an
d
id
le/ac
ti
v
e
s
tate
s
o
f
p
o
r
t
ab
le
m
icr
o
p
r
o
ce
s
s
o
r
s
.
Sig
n
i
f
i
ca
n
t
p
o
w
er
s
av
in
g
s
ca
n
b
e
o
b
tain
ed
b
y
o
p
ti
m
a
l
s
elec
tio
n
o
f
V
th
a
n
d
V
DD
f
o
r
m
an
y
cir
c
u
it
ar
ch
itect
u
r
es,
o
p
er
atin
g
e
n
v
ir
o
n
m
en
t
an
d
ad
ap
tiv
el
y
t
u
n
i
n
g
t
h
ese
v
alu
e
s
b
u
il
t o
n
w
o
r
k
lo
ad
v
ar
ia
tio
n
s
i
n
a
r
u
n
ti
m
e
m
a
n
n
er
.
Si
m
u
latio
n
r
es
u
lts
i
n
u
s
i
n
g
P
F
-
P
SO
f
o
r
p
o
w
er
d
is
s
ip
atio
n
r
ed
u
ctio
n
in
m
icr
o
p
r
o
ce
s
s
o
r
u
n
d
er
test
th
at
ac
ce
p
tin
g
j
o
in
t
V
th
-
V
DD
c
o
n
f
ir
m
ed
s
atis
f
ied
ac
h
iev
e
m
e
n
ts
t
h
at
e
f
f
icien
t
l
y
to
ler
ate
w
o
r
k
lo
ad
v
ar
iatio
n
s
o
n
th
e
v
o
lta
g
e,
f
r
eq
u
en
c
y
,
an
d
t
e
m
p
er
atu
r
e
v
ar
iat
io
n
s
w
i
th
a
m
i
n
i
m
al
p
en
alt
y
i
n
p
er
f
o
r
m
an
ce
r
eq
u
ir
e
m
e
n
t
s
.
T
h
er
ef
o
r
e,
w
h
e
n
th
e
p
r
o
ce
s
s
o
r
en
ab
les
o
p
er
atio
n
at
lo
w
e
r
s
u
p
p
l
y
v
o
lta
g
e
u
n
d
er
th
e
s
a
m
e
e
n
v
ir
o
n
m
e
n
ta
l
v
ar
iatio
n
,
it
h
elp
s
r
ed
u
ce
th
e
clo
c
k
f
r
eq
u
e
n
c
y
m
ar
g
i
n
f
o
r
lo
w
er
p
o
w
er
d
i
s
s
ip
a
tio
n
r
eq
u
ir
e
m
e
n
ts
.
T
h
e
tech
n
iq
u
e
al
lo
w
s
t
h
e
o
v
er
all
s
y
s
te
m
to
o
p
er
ate
in
a
w
id
e
v
o
lta
g
e
a
n
d
f
r
eq
u
e
n
c
y
d
o
m
a
in
w
h
ile
m
ai
n
tai
n
in
g
t
h
e
ch
ip
p
o
w
er
d
i
s
s
ip
atio
n
.
5.
CO
NCLU
SI
O
N
T
h
e
r
esu
lts
s
h
o
w
ed
t
h
at,
th
e
m
icr
o
p
r
o
ce
s
s
o
r
d
ev
ice
d
is
s
ip
ates
an
o
p
ti
m
al
a
m
o
u
n
t
o
f
p
o
w
er
d
is
s
ip
atio
n
w
h
en
t
h
e
j
o
in
t
th
r
esh
o
ld
-
s
u
p
p
l
y
s
ca
lin
g
(
DVS
-
B
B
VS)
is
ap
p
lied
o
n
a
d
y
n
a
m
ic
co
m
p
u
tatio
n
w
o
r
k
lo
ad
at
d
i
f
f
er
e
n
t
e
x
ec
u
ti
o
n
ti
m
e
s
to
atte
n
d
a
h
i
g
h
p
er
f
o
r
m
an
ce
e
s
ti
m
atio
n
.
C
o
n
s
e
q
u
en
tl
y
,
it
v
er
if
ied
th
e
u
s
e
o
f
P
F
-
P
SO a
p
p
r
o
ac
h
f
o
r
j
o
in
t V
th
-
V
dd
s
ca
li
n
g
f
o
r
p
o
w
er
a
n
d
te
m
p
er
atu
r
e
a
w
ar
e
d
e
s
ig
n
w
h
ic
h
is
e
v
er
y
ef
f
icien
t
to
th
e
p
r
o
ce
s
s
o
r
ar
ch
itect
an
d
d
esig
n
er
s
i
n
f
o
r
m
ali
zin
g
t
h
eir
v
er
if
ica
tio
n
s
b
ec
a
u
s
e
o
f
th
e
w
o
r
k
lo
ad
p
ar
allel
n
atu
r
e
a
n
d
ac
tiv
e
-
id
le
m
o
d
es
o
f
p
r
o
ce
s
s
o
r
s
.
S
u
b
s
eq
u
en
t
l
y
,
t
h
e
P
F
-
P
SO
ap
p
r
o
ac
h
esti
m
ated
o
p
ti
m
al
en
er
g
y
s
a
v
i
n
g
s
w
h
en
ap
p
l
y
i
n
g
a
co
r
r
ec
t
b
o
d
y
a
n
d
s
u
p
p
l
y
v
o
ltag
e
s
f
o
r
d
if
f
er
e
n
t
o
p
er
atin
g
d
o
m
ain
a
n
d
d
if
f
er
e
n
t
cir
c
u
it
d
e
s
i
g
n
s
o
n
th
e
n
at
u
r
e
o
f
t
h
e
w
o
r
k
lo
ad
d
y
n
a
m
ic
v
ar
iatio
n
s
d
u
r
i
n
g
r
ea
l
o
p
er
atio
n
s
.
Fin
all
y
,
PF
-
P
SO
ap
p
r
o
ac
h
r
es
u
lts
s
h
o
w
ed
th
at,
th
e
j
o
in
t
V
th
-
V
DD
ca
n
p
er
f
o
r
m
a
lar
g
e
a
s
p
ec
t
in
t
h
e
o
p
ti
m
al
p
o
w
er
ac
h
iev
e
m
e
n
t
s
,
th
u
s
it
s
ef
f
ec
t
in
cr
ea
s
e
s
as
tec
h
n
o
lo
g
y
is
s
ca
led
d
o
w
n
to
th
e
d
ee
p
n
an
o
m
eter
p
r
o
ce
s
s
.
I
t
is
co
n
f
ir
m
ed
,
a
p
o
ten
tial
r
e
d
u
ctio
n
o
f
th
e
d
is
s
ip
ated
p
o
w
er
w
as
i
n
t
h
e
r
an
g
e
o
f
(
2
.
9
5
4
%)
u
p
to
(
1
5
.
4
7
8
%),
an
d
r
ed
u
ctio
n
o
f
th
e
te
m
p
er
at
u
r
es
w
as
in
th
e
r
a
n
g
e
o
f
(
2
o
C
)
f
o
r
ea
ch
s
tep
in
t
h
e
b
o
d
y
an
d
s
u
p
p
l
y
v
o
lta
g
e
v
ar
iatio
n
t
h
at
led
to
a
p
o
w
er
f
u
l i
m
p
r
o
v
e
m
en
t i
n
p
o
w
er
d
is
s
ip
atio
n
an
d
as c
o
m
p
ar
ed
to
th
e
p
r
ev
io
u
s
s
tu
d
ie
s
.
RE
F
E
R
E
NC
E
S
[1
]
A
.
Kh
a
ta
k
,
e
t
a
l.
,
“
A
n
a
l
y
sis
o
f
CM
OS
Co
m
p
a
ra
to
r
in
9
0
n
m
T
e
c
h
n
o
lo
g
y
w
it
h
Diffe
re
n
t
P
o
w
e
r
Re
d
u
c
ti
o
n
T
e
c
h
n
iq
u
e
s
,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
r
n
a
l
o
f
El
e
c
trica
l
a
n
d
C
o
mp
u
ter
En
g
in
e
e
rin
g
(
IJ
ECE
),
v
o
l.
8
,
n
o
.
6
,
p
p
.
4
9
2
2
-
4
9
3
1
,
2
0
1
8
.
[2
]
A
.
K.
Da
d
o
ria,
e
t
a
l.
,
“
A
No
v
e
l
Ap
p
r
o
a
c
h
f
o
r
L
e
a
k
a
g
e
P
o
w
e
r
Re
d
u
c
ti
o
n
i
n
De
e
p
S
b
m
icro
n
T
e
c
h
n
o
lo
g
ies
in
CM
OS
V
L
S
I
Circu
it
s,
”
2
0
1
5
In
ter
n
a
ti
o
n
a
l
Co
n
f
e
re
n
c
e
o
n
Co
m
p
u
ter
,
C
o
mm
u
n
ica
ti
o
n
a
n
d
C
o
n
tr
o
l
(
IC4
)
,
p
p
.
1
-
6
,
2
0
1
5
.
[3
]
A
.
H
a
sa
n
b
e
g
o
v
i
,
“
Ex
p
lo
rin
g
th
e
S
EU
De
p
e
n
d
e
n
c
e
o
n
S
u
p
p
ly
V
o
lt
a
g
e
S
c
a
li
n
g
in
9
0
n
m
a
n
d
6
5
n
m
CM
OS
F
li
p
-
fl
o
p
s
,
”
P
h
D T
h
e
sis
,
Un
iv
e
rsity
o
f
Os
lo
,
2
0
1
7
.
[4
]
R.
Ch
e
n
g
,
e
t
a
l.
,
“
T
e
st
p
ro
b
l
e
m
s
f
o
r
larg
e
-
s
c
a
l
e
m
u
lt
io
b
jec
ti
v
e
a
n
d
m
a
n
y
-
o
b
jec
ti
v
e
o
p
ti
m
iza
ti
o
n
,
”
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
Cy
b
e
rn
e
ti
c
s
,
v
o
l.
4
7
,
n
o
.
1
2
,
p
p
.
4
1
0
8
-
4
1
2
1
,
2
0
1
7
.
[5
]
H.
F
.
S
h
e
ik
h
,
e
t
a
l.
,
“
A
n
Ev
o
l
u
ti
o
n
a
ry
Tec
h
n
iq
u
e
f
o
r
P
e
rf
o
rm
a
n
c
e
-
En
e
rg
y
-
T
e
m
p
e
ra
tu
re
Op
ti
m
iz
e
d
S
c
h
e
d
u
li
n
g
o
f
P
a
ra
ll
e
l
T
a
sk
s
o
n
M
u
lt
i
-
C
o
re
P
ro
c
e
ss
o
rs,
”
IEE
E
T
ra
n
s
a
c
ti
o
n
s
o
n
Pa
ra
ll
e
l
a
n
d
Distrib
u
ted
S
y
ste
ms
,
v
o
l.
2
7
,
n
o
.
3
,
p
p
.
6
6
8
-
6
8
1
,
2
0
1
6
.
[6
]
V
.
L
.
Ra
n
i
a
n
d
M
.
M
.
L
a
th
a
,
“
P
a
rti
c
le
S
w
a
r
m
Op
ti
m
iza
ti
o
n
(
P
S
A
)
A
l
g
o
rit
h
m
f
o
r
L
e
a
k
a
g
e
P
o
w
e
r
Re
d
u
c
ti
o
n
i
n
V
L
S
I
Circu
it
s,
”
I
n
t
e
rn
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
El
e
c
tro
n
ics
a
n
d
T
e
lec
o
mm
u
n
ica
ti
o
n
s,
v
o
l.
6
2
,
n
o
.
2
,
p
p
.
1
7
9
-
1
8
6
,
2
0
1
6
.
[7
]
K.
M
.
A
tt
ia
,
e
t
a
l.
,
“
D
y
n
a
m
ic
p
o
w
e
r
m
a
n
a
g
e
m
e
n
t
tec
h
n
iq
u
e
s
in
m
u
lt
i
-
c
o
re
a
rc
h
it
e
c
tu
r
e
s:
A
su
rv
e
y
stu
d
y
,
”
Ai
n
S
h
a
ms
En
g
in
e
e
rin
g
J
o
u
rn
a
l
,
v
o
l.
8
,
n
o
.
3
,
p
p
.
4
4
5
-
4
5
6
,
2
0
1
7
.
[8
]
D.
S
u
laim
a
n
,
e
t
a
l.
,
“
M
icro
p
r
o
c
e
ss
o
rs
o
p
ti
m
a
l
p
o
w
e
r
d
issip
a
ti
o
n
u
sin
g
c
o
m
b
in
e
d
th
re
sh
o
l
d
h
o
p
p
i
n
g
a
n
d
v
o
lt
a
g
e
sc
a
li
n
g
,
”
IEI
CE
El
e
c
tro
n
ics
Exp
r
e
ss
,
v
o
l.
1
4
,
n
o
.
2
4
,
p
p
.
2
0
1
7
1
0
4
6
,
2
0
1
7
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2
0
8
8
-
8708
Mu
lti
-
o
b
jective
P
F
a
n
d
P
S
O
a
lg
o
r
ith
ms fo
r
p
o
w
er
.
..
(
Dia
r
y
R
.
S
u
la
ima
n
)
6557
[9
]
A
.
Ku
m
a
r
a
n
d
R.
K.
Na
g
a
ria,
“
A
n
e
w
lea
k
a
g
e
-
to
lera
n
t
h
ig
h
sp
e
e
d
c
o
m
p
a
ra
to
r
b
a
se
d
d
o
m
in
o
g
a
te
fo
r
w
id
e
f
a
n
-
i
n
OR l
o
g
ic f
o
r
lo
w
p
o
w
e
r
V
L
S
I
c
ircu
it
s,
”
In
teg
r
a
ti
o
n
,
v
o
l.
6
3
,
p
p
.
1
7
4
-
1
8
4
,
2
0
1
8
.
[1
0
]
P
.
S
in
g
h
,
e
t
a
l.
,
“
Ultra
lo
w
p
o
we
r
-
h
ig
h
sta
b
il
it
y
,
p
o
siti
v
e
f
e
e
d
b
a
c
k
c
o
n
tro
ll
e
d
(
P
F
C)
1
0
T
S
RA
M
c
e
ll
f
o
r
lo
o
k
u
p
tab
le (L
UT
)
d
e
sig
n
,
”
In
teg
ra
ti
on
t
h
e
VL
S
I
J
o
u
r
n
a
l
,
v
o
l.
6
2
,
p
p
.
1
-
1
3
,
2
0
1
8
.
[1
1
]
Y.
W
a
n
g
,
e
t
a
l.
,
“
Ex
p
e
rime
n
tal
Ch
a
ra
c
teriz
a
ti
o
n
o
f
V
a
riati
o
n
i
n
P
o
w
e
r
Co
n
su
m
p
ti
o
n
f
o
r
P
ro
c
e
ss
o
rs
o
f
Diff
e
re
n
t
G
e
n
e
r
a
ti
o
n
s,
”
I
n
ter
n
a
t
io
n
a
l
Co
n
fer
e
n
c
e
o
n
In
ter
n
e
t
o
f
T
h
in
g
s
(
iT
h
in
g
s)
a
n
d
IEE
E
Gr
e
e
n
Co
mp
u
t
in
g
a
n
d
Co
mm
u
n
ica
ti
o
n
s
(
Gr
e
e
n
Co
m)
a
n
d
IEE
E
Cy
b
e
r,
P
h
y
sic
a
l
a
n
d
S
o
c
i
a
l
Co
mp
u
ti
n
g
(
CPS
Co
m)
a
n
d
IE
EE
S
ma
rt
Da
t
a
(
S
ma
rtDa
ta
)
,
p
p
.
7
0
2
-
7
1
0
,
2
0
1
9
.
[1
2
]
E.
A
n
g
e
l,
e
t
a
l.
,
“
S
p
e
e
d
sc
a
li
n
g
o
n
p
a
ra
ll
e
l
p
ro
c
e
ss
o
rs
w
it
h
m
ig
r
a
ti
o
n
,
”
J
o
u
rn
a
l
o
f
C
o
mb
i
n
a
to
ria
l
Op
ti
miza
ti
o
n
,
v
o
l.
3
7
,
p
p
.
1
2
6
6
-
1
2
8
2
,
2
0
1
9
.
[1
3
]
I.
P
.
V
a
isb
a
n
d
,
e
t
a
l.
,
“
On
-
C
h
ip
P
o
w
e
r
De
li
v
e
r
y
a
n
d
M
a
n
a
g
e
m
e
n
t,
”
S
p
ri
n
g
e
r In
ter
n
a
ti
o
n
a
l
Pu
b
li
sh
i
n
g
,
2
0
1
6
.
[1
4
]
J.
N.
M
istry
,
“
Lea
k
a
g
e
p
o
w
e
r
m
in
i
m
isa
ti
o
n
tec
h
n
i
q
u
e
s
f
o
r
e
m
b
e
d
d
e
d
p
r
o
c
e
ss
o
rs,
”
P
h
D
T
h
e
sis,
Un
iv
e
rsity
o
f
S
o
u
t
h
a
m
p
to
n
,
2
0
1
3
.
[1
5
]
G
.
G
u
p
ta
a
n
d
R.
M
e
h
ra
,
“
M
OSF
ET
su
b
-
th
re
sh
o
l
d
c
u
rre
n
t
r
e
d
u
c
ti
o
n
b
y
v
a
r
y
in
g
su
b
stra
te
d
o
p
in
g
,
”
IEE
E
In
ter
n
a
t
io
n
a
l
C
o
n
fer
e
n
c
e
o
n
A
d
v
a
n
c
e
d
C
o
mm
u
n
ic
a
ti
o
n
Co
n
tr
o
l
a
n
d
C
o
mp
u
ti
n
g
T
e
c
h
n
o
lo
g
ie
s
(
ICACCCT
)
,
p
p
.
5
5
1
-
5
5
4
,
2
0
1
4
.
[1
6
]
J.
Ya
o
,
“
Du
a
l
-
T
h
re
sh
o
l
d
V
o
l
tag
e
De
sig
n
o
f
S
u
b
-
T
h
re
sh
o
l
d
Circu
i
ts,
”
P
h
D T
h
e
sis,
A
u
b
u
r
n
Un
iv
e
rsity
,
2
0
1
4
.
[1
7
]
W
.
W
.
Ka
i,
e
t
a
l.
,
“
V
a
riab
le
B
o
d
y
Bias
in
g
(V
BB)
b
a
se
d
V
L
S
I
De
sig
n
A
p
p
ro
a
c
h
t
o
Re
d
u
c
e
S
tatic
P
o
w
e
r,
”
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
E
lec
trica
l
a
n
d
C
o
mp
u
ter
En
g
in
e
e
rin
g
(
IJ
ECE
)
,
v
o
l.
7
,
n
o
.
6
,
p
p
.
3
0
1
0
-
3
0
1
9
,
2
0
1
7
.
[1
8
]
L
.
W
a
n
g
,
e
t
a
l.
,
“
A
L
o
w
-
P
o
w
e
r
F
o
rw
a
rd
a
n
d
Re
v
e
rse
Bo
d
y
Bias
G
e
n
e
r
a
to
r
in
C
M
OS
4
0
n
m
,
”
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
Ver
y
L
a
rg
e
S
c
a
le In
te
g
ra
ti
o
n
(
VL
S
I)
S
y
ste
ms
,
v
o
l.
2
6
,
n
o
.
7
,
p
p
.
1
4
0
3
-
1
4
0
7
,
2
0
1
8
.
[1
9
]
H.
G
o
y
a
l,
“
Ch
a
ra
c
teriz
in
g
P
ro
c
e
ss
o
rs f
o
r
T
i
m
e
a
n
d
En
e
rg
y
Op
ti
m
i
z
a
ti
o
n
,
”
M
S
c
T
h
e
sis,
A
u
b
u
rn
U
n
i
v
e
rsit
y
,
2
0
1
6
.
[2
0
]
C.
Nd
iay
e
,
e
t
a
l.
,
“
P
e
rf
o
r
m
a
n
c
e
v
s.
re
li
a
b
il
it
y
a
d
a
p
ti
v
e
b
o
d
y
b
ias
sc
h
e
m
e
in
2
8
n
m
&
1
4
n
m
U
T
BB
F
DSOI
n
o
d
e
s,
”
M
icr
o
e
lec
tro
n
ics
Relia
b
il
it
y
,
v
o
l.
6
4
,
pp
.
1
5
8
-
1
6
2
,
2
0
1
6
.
[2
1
]
U.
Ba
u
m
g
a
rtn
e
r,
e
t
a
l.
,
“
P
a
re
to
Op
ti
m
a
li
t
y
a
n
d
P
a
rti
c
le
S
w
a
r
m
Op
ti
m
iza
ti
o
n
,
”
IEE
E
T
ra
n
s
a
c
ti
o
n
s
o
n
M
a
g
n
e
ti
c
s
,
v
o
l.
4
0
,
n
o
.
2
,
p
p
.
1
1
7
2
-
1
1
7
5
,
2
0
0
4
.
[2
2
]
D.
E.
G
o
ld
b
e
rg
,
“
G
e
n
e
ti
c
A
lg
o
rit
h
m
s
in
S
e
a
r
c
h
,
Op
ti
m
iz
a
ti
o
n
,
a
n
d
M
a
c
h
in
e
L
e
a
rn
in
g
,
”
A
d
d
iso
n
-
W
e
sle
y
L
o
n
g
ma
n
Pu
b
li
s
h
in
g
,
1
9
8
9
.
[2
3
]
G
.
C.
Ca
rd
a
ril
li
,
e
t
a
l.
,
“
En
e
rg
y
Co
n
su
m
p
ti
o
n
S
a
v
in
g
i
n
Em
b
e
d
d
e
d
M
icr
o
p
r
o
c
e
ss
o
rs
Us
in
g
Ha
rd
w
a
r
e
A
c
c
e
lera
to
rs,
”
T
EL
KOM
NIKA
T
e
lec
o
mm
u
n
ica
ti
o
n
,
Co
m
p
u
ti
n
g
,
El
e
c
tro
n
ics
a
n
d
Co
n
tro
l,
v
o
l.
1
6
,
n
o
.
3
,
p
p
.
1
0
1
9
-
1
0
2
6
,
2
0
1
8
.
[2
4
]
J.
Ke
n
n
e
d
y
a
n
d
R.
Eb
e
rh
a
rt,
“
P
a
r
ti
c
le
s
w
a
r
m
o
p
ti
m
iza
ti
o
n
,
”
Pro
c
e
e
d
in
g
s
o
f
t
h
e
IEE
E
In
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
Ne
u
ra
l
Ne
two
rk
s
,
v
o
l.
4
,
p
p
.
1
9
4
2
-
1
9
4
8
,
1
9
9
5
.
[2
5
]
M
.
A
.
A
ra
so
m
w
a
n
a
n
d
A
.
O.
A
d
e
w
u
m
i,
“
On
th
e
p
e
rf
o
rm
a
n
c
e
o
f
li
n
e
a
r
d
e
c
re
a
sin
g
in
e
rti
a
w
e
ig
h
t
p
a
rti
c
le
sw
a
r
m
o
p
ti
m
iza
ti
o
n
f
o
r
g
lo
b
a
l
o
p
ti
m
iza
ti
o
n
,
”
T
h
e
S
c
ie
n
ti
f
ic W
o
rl
d
J
o
u
rn
a
l
,
v
o
l.
2
0
1
3
,
p
p
.
1
-
1
2
,
2
0
1
3
.
[2
6
]
T
.
W
in
a
rn
o
,
e
t
a
l.
,
“
M
P
P
T
c
o
n
tro
l
o
f
P
V
a
rra
y
b
a
se
d
o
n
P
S
O
a
n
d
a
d
a
p
t
iv
e
c
o
n
tr
o
ll
e
r,
”
T
EL
KOM
NIKA
T
e
lec
o
mm
u
n
ica
ti
o
n
,
Co
m
p
u
t
in
g
,
El
e
c
tro
n
ics
a
n
d
C
o
n
tro
l
,
v
o
l.
1
8
,
n
o
.
2
,
p
p
.
1
1
1
3
-
1
1
2
1
,
2
0
2
0
.
[2
7
]
V
.
L
.
Ra
n
i
a
n
d
M
.
M
.
L
a
th
a
,
“
P
a
rti
c
le
S
w
a
r
m
Op
ti
m
iz
a
ti
o
n
A
l
g
o
rit
h
m
f
o
r
L
e
a
k
a
g
e
P
o
w
e
r
R
e
d
u
c
ti
o
n
in
V
L
S
I
Circu
it
s,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
El
e
c
tro
n
ics
a
n
d
T
e
lec
o
mm
u
n
ica
ti
o
n
s
,
v
o
l.
6
2
,
n
o
.
2
,
p
p
.
1
7
9
-
1
8
6
,
2
0
1
6
.
[2
8
]
F
.
M
a
rin
i
a
n
d
B.
W
a
lcz
a
k
,
“
Pa
rti
c
le
s
w
a
r
m
o
p
ti
m
iz
a
ti
o
n
(
P
S
O)
:
A
tu
to
rial,
”
Ch
e
mo
me
trics
a
n
d
In
telli
g
e
n
t
L
a
b
o
ra
t
o
ry
S
y
ste
ms
,
v
o
l.
1
4
9
,
p
p
.
1
5
3
-
1
6
5
,
2
0
1
5
.
[2
9
]
N.
Dib
a
n
d
U.
A
l
-
S
a
m
m
a
rra
i
e
,
“
Op
ti
m
a
l
d
e
sig
n
o
f
s
y
m
m
e
tri
c
s
w
i
tch
in
g
CM
OS
i
n
v
e
rter
u
sin
g
sy
m
b
io
t
ic o
rg
a
n
ism
s
se
a
rc
h
a
lg
o
rit
h
m
,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
El
e
c
trica
l
a
n
d
C
o
mp
u
ter
En
g
i
n
e
e
rin
g
(
IJ
ECE
)
,
v
o
l.
1
0
,
n
o
.
1
,
p
p
.
1
7
1
-
1
7
9
,
2
0
2
0
.
[3
0
]
W
.
R.
A
b
d
u
l
-
A
d
h
e
e
m
,
“
A
n
e
n
h
a
n
c
e
d
p
a
rti
c
le
sw
a
r
m
o
p
ti
m
iza
ti
o
n
a
lg
o
rit
h
m
,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
El
e
c
trica
l
a
n
d
Co
m
p
u
ter
E
n
g
i
n
e
e
rin
g
(
IJ
ECE
),
v
o
l.
9
,
n
o
.
6
,
p
p
.
4
9
0
4
-
4
9
0
7
,
2
0
1
9
.
B
I
O
G
RAP
H
Y
O
F
AUTHO
R
Dia
r
y
R.
S
u
la
i
m
a
n
,
a
ss
istan
t
P
ro
f
e
ss
o
r
o
f
El
e
c
tro
n
ics
a
n
d
Co
m
p
u
ter
S
y
ste
m
s
En
g
in
e
e
rin
g
,
De
p
a
rtme
n
t
o
f
El
e
c
tri
c
a
l
En
g
in
e
e
rin
g
,
Co
ll
e
g
e
o
f
En
g
in
e
e
rin
g
,
S
a
lah
a
d
d
i
n
u
n
iv
e
rsity
-
Erb
il
,
Ira
q
.
Dia
r
y
R.
S
U
LA
IM
A
N
wa
s
b
o
rn
in
Ra
n
ia
Cit
y
,
S
u
la
y
m
a
n
i
y
a
h
,
IRA
Q,
in
1
9
6
7
.
He
re
c
e
iv
e
d
th
e
M
.
S
c
.
,
a
n
d
P
h
.
D
d
e
g
re
e
in
e
lec
tri
c
a
l
e
n
g
in
e
e
rin
g
f
ro
m
th
e
S
a
lah
a
d
d
in
U
n
iv
e
rsity
-
Erb
il
,
IRA
Q.
His
c
u
rre
n
t
re
se
a
rc
h
in
tere
sts
in
c
lu
d
e
p
o
w
e
r/t
h
e
r
m
a
l
m
a
n
a
g
e
m
e
n
t
o
f
m
icro
p
ro
c
e
ss
o
rs,
a
d
v
a
n
c
e
d
d
ig
it
a
l
d
e
sig
n
,
a
n
d
CM
OS
c
ircu
it
d
e
sig
n
.
Dia
r
y
R.
S
U
LIA
IM
A
N
h
a
s
a
u
th
o
re
d
m
a
n
y
p
u
b
li
c
a
ti
o
n
s
o
n
e
lec
tro
n
ics
a
n
d
c
o
m
p
u
ter
h
a
rd
w
a
re
d
e
sig
n
,
CM
OS
c
ircu
it
d
e
sig
n
a
n
d
m
icro
p
ro
c
e
ss
o
rs
p
o
w
e
r/t
h
e
r
m
a
l
m
a
n
a
g
e
m
e
n
t.
He
h
a
s
m
o
re
th
a
n
4
0
p
u
b
li
sh
e
d
a
rti
c
les
in
in
tern
a
ti
o
n
a
l
j
o
u
r
n
a
ls an
d
c
o
n
f
e
re
n
c
e
s.
Evaluation Warning : The document was created with Spire.PDF for Python.