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.
i
n
1.
I
NT
RO
D
UCT
I
O
N
Ad
ap
tiv
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f
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s
ad
ju
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t
th
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an
s
f
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f
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n
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s
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Am
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th
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least
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s
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(
L
MS)
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licity
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atr
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io
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g
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d
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v
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g
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tim
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ip
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im
p
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tatio
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at
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s
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p
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g
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ates
an
d
s
en
s
itiv
ity
to
in
p
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t
s
ca
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g
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m
p
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lear
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ate
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elec
tio
n
.
T
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e
d
ela
y
ed
least m
ea
n
s
q
u
ar
e
(
DL
MS)
ar
c
h
itectu
r
e
i
n
[
1
]
d
em
o
n
s
tr
ated
ef
f
ec
tiv
e
er
r
o
r
c
o
n
v
er
g
en
ce
th
r
o
u
g
h
MA
T
L
AB
®
Simu
lin
k
m
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d
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g
.
T
h
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p
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p
o
r
tio
n
ate
least
m
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n
s
q
u
ar
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(
PLM
S)
ar
ch
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r
e
in
[
2
]
f
u
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th
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clar
if
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PLM
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f
u
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d
am
en
tals
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en
ab
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a
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s
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u
latio
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s
.
Alth
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u
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th
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n
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v
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d
esig
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in
[
3
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ac
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iev
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p
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a
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d
tim
in
g
ef
f
icien
cy
,
it
r
eq
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a.
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s
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r
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,
w
h
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p
tim
izes
ar
ea
at
a
s
lig
h
t tim
in
g
co
m
p
lex
ity
c
o
s
t.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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2
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8
8
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I
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t J E
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&
C
o
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,
Vo
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15
,
No
.
2
,
Ap
r
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20
25
:
2
5
1
3
-
2
5
2
2
2514
R
ep
lacin
g
m
u
ltip
lier
s
with
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o
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ar
ith
m
ic
an
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a
n
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lo
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ar
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m
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co
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p
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tatio
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s
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as
d
is
cu
s
s
ed
in
[
4
]
,
im
p
r
o
v
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d
tim
e
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s
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.
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s
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8
-
b
it
Ve
d
ic
m
u
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f
r
o
m
[
5
]
–
[
1
2
]
f
o
r
b
etter
laten
cy
an
d
p
o
wer
p
er
f
o
r
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ce
.
Ad
d
e
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s
elec
tio
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in
f
o
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m
ed
b
y
[
1
3
]
–
[
2
0
]
,
an
d
in
s
ig
h
ts
f
r
o
m
s
y
s
to
lic
ar
ch
itectu
r
es
in
[
2
1
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–
[
2
9
]
c
o
n
tr
ib
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ted
to
o
u
r
d
esig
n
'
s
f
a
s
ter
co
n
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ce
.
Stu
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[
3
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]
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[
3
1
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h
ig
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ted
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ac
cu
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ith
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2.
RE
S
E
ARCH
M
E
T
H
O
D
T
h
e
b
lo
ck
d
ia
g
r
am
s
h
o
wn
i
n
Fig
u
r
e
s
1
(
a)
an
d
1
(
b
)
r
ep
r
e
s
en
ts
th
e
b
lo
ck
d
iag
r
am
o
f
th
e
ad
ap
tiv
e
f
ilter
as
an
u
n
k
n
o
wn
s
y
s
tem
id
en
tifie
r
an
d
c
o
n
v
e
r
g
en
ce
g
r
a
p
h
f
o
r
d
if
f
er
e
n
t
alg
o
r
ith
m
r
esp
ec
tiv
ely
.
B
o
th
th
e
ad
ap
tiv
e
f
ilter
as
well
as
th
e
u
n
k
n
o
wn
s
y
s
tem
a
r
e
g
iv
en
th
e
s
am
e
in
p
u
ts
.
T
h
e
o
u
t
p
u
t
t
h
a
t
o
cc
u
r
s
ac
r
o
s
s
th
e
u
n
k
n
o
wn
s
y
s
tem
will b
e
th
e
d
esire
d
s
ig
n
al
(
)
.
T
h
e
in
p
u
t
v
ec
to
r
(
)
is
th
e
r
esu
lt
o
f
f
u
r
th
er
e
n
co
d
i
n
g
th
e
in
p
u
t
p
r
o
v
id
e
d
b
y
th
e
ad
ap
tiv
e
f
ilter
in
to
d
ig
ital
b
in
ar
y
d
ata.
I
n
th
e
f
ilter
,
th
e
tap
len
g
th
d
eter
m
in
es
th
e
f
ilter
'
s
o
r
d
er
.
Simp
le
co
n
v
o
lu
tio
n
is
u
s
ed
to
ca
lcu
late
th
e
ad
ap
tiv
e
f
ilter
's
o
u
tp
u
t,
wh
ich
will
in
itiall
y
h
av
e
s
o
m
e
u
n
k
n
o
wn
wei
g
h
ts
.
I
f
th
er
e
is
a
d
is
cr
ep
an
cy
b
etwe
en
th
is
o
u
t
p
u
t
an
d
th
e
in
ten
d
ed
o
u
tp
u
t,
it
is
p
as
s
ed
b
ac
k
to
th
e
weig
h
t
u
p
d
ate
b
lo
c
k
to
p
r
o
v
id
e
n
ew
co
e
f
f
icien
ts
.
(
a)
(
b
)
Fig
u
r
e
1
.
An
al
y
s
is
an
d
p
er
f
o
r
m
an
ce
ev
alu
atio
n
o
f
t
h
e
ad
a
p
tiv
e
f
ilter
s
y
s
tem
: (
a)
b
lo
c
k
d
iag
r
am
o
f
t
h
e
ad
ap
tiv
e
f
ilter
illu
s
tr
atin
g
th
e
k
ey
co
m
p
o
n
e
n
ts
an
d
s
ig
n
al
f
l
o
w
an
d
(
b
)
c
o
n
v
er
g
e
n
ce
g
r
ap
h
co
m
p
ar
in
g
th
e
p
er
f
o
r
m
an
ce
o
f
d
if
f
er
en
t a
lg
o
r
ith
m
s
in
ter
m
s
o
f
er
r
o
r
r
ed
u
cti
o
n
o
v
e
r
iter
atio
n
s
T
h
is
p
r
o
ce
s
s
co
n
tin
u
es
to
h
ap
p
en
till
th
e
er
r
o
r
s
ig
n
al
id
ea
lly
g
o
es
d
o
wn
to
ze
r
o
.
I
f
th
e
er
r
o
r
s
ig
n
al
is
ze
r
o
it im
p
lies
th
at:
a.
T
h
e
o
u
tp
u
t
o
f
t
h
e
ad
a
p
tiv
e
f
il
ter
is
s
am
e
as
th
at
o
f
th
e
o
u
t
p
u
t
o
f
th
e
u
n
k
n
o
wn
s
y
s
tem
,
i.e
.
(
)
=
(
)
(
b
ec
au
s
e
(
)
=
(
)
−
(
)
)
.
b.
If
(
)
=
(
)
it
s
u
g
g
ests
th
at
th
e
ad
ap
tiv
e
f
ilter
is
p
r
o
d
u
cin
g
th
e
s
am
e
o
u
tp
u
t
as
th
e
u
n
k
n
o
wn
s
y
s
tem
f
o
r
a
g
iv
en
in
p
u
t
an
d
h
en
ce
t
h
e
co
ef
f
icie
n
ts
o
f
b
o
th
th
e
u
n
k
n
o
wn
s
y
s
tem
a
n
d
th
e
ad
ap
ti
v
e
f
ilter
ar
e
th
e
s
am
e.
So
th
er
ef
o
r
e,
th
e
a
d
ap
ti
v
e
f
ilter
is
s
aid
to
h
av
e
i
d
en
tifi
ed
th
e
u
n
k
n
o
wn
s
y
s
tem
u
n
d
e
r
test
.
T
h
e
m
ath
em
atica
l
eq
u
atio
n
s
th
at
will
b
e
u
s
ed
to
f
in
d
o
u
t
th
e
f
ilter
o
u
tp
u
t,
th
e
er
r
o
r
s
ig
n
al
an
d
th
e
u
p
d
ated
weig
h
ts
ar
e
as g
iv
en
in
T
ab
le
1
.
T
ab
le
1
.
Ad
a
p
tiv
e
f
ilter
eq
u
atio
n
s
F
u
n
c
t
i
o
n
Eq
u
a
t
i
o
n
I
n
p
u
t
v
e
c
t
o
r
(
)
=
[
(
)
,
(
−
1
)
,
.
.
.
.
,
(
−
+
1
)
]
F
i
l
t
e
r
o
u
t
p
u
t
(
)
=
(
)
(
)
Er
r
o
r
si
g
n
a
l
ma
t
r
i
x
(
)
=
(
)
−
(
)
U
p
d
a
t
e
d
w
e
i
g
h
t
s
(
+
1
)
=
(
)
+
(
)
(
)
(
)
I
d
e
n
t
i
t
y
m
a
t
r
i
x
(
)
=
N
o
t
e
:
(
)
i
s s
e
t
o
f
c
u
r
r
e
n
t
w
e
i
g
h
t
s,
i
s
a
d
a
p
t
i
v
e
s
t
e
p
si
z
e
,
a
n
d
(
)
i
s
g
a
i
n
ma
t
r
i
x
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:
2088
-
8
7
0
8
E
fficien
t p
o
w
er o
p
timiz
ed
ve
r
y
-
la
r
g
e
-
s
ca
le
in
teg
r
a
tio
n
…
(
Ga
n
g
a
d
h
a
r
a
ia
h
S
o
r
a
la
m
a
vu
L
a
ksh
ma
ia
h
)
2515
T
h
e
n
o
v
el
im
p
l
em
en
tat
io
n
s
o
n
o
u
r
d
esi
g
n
wo
u
l
d
b
e:
i)
A
p
r
o
p
o
s
e
d
f
l
o
at
in
g
p
o
i
n
t
m
o
d
u
le
a
p
p
r
o
a
c
h
f
o
r
th
e
P
L
MS
r
eg
is
te
r
t
r
a
n
s
f
e
r
l
e
v
el
im
p
l
em
en
tat
io
n
a
n
d
ii)
I
m
p
l
em
e
n
t
in
g
a
n
a
r
e
a
ef
f
i
cie
n
t
a
r
c
h
it
ec
t
u
r
e
f
o
r
PLM
S
alg
o
r
it
h
m
b
as
e
d
ad
a
p
ti
v
e
f
ilt
er
im
p
l
em
e
n
ta
ti
o
n
o
n
FP
G
A'
s
.
T
h
e
PL
MS
u
p
d
at
e
eq
u
ati
o
n
is
g
i
v
e
n
b
y
(
1
)
:
(
+
1
)
=
(
)
+
(
)
(
)
(
)
(
1
)
T
h
e
Pt
-
NL
MS
f
am
ily
o
f
al
g
o
r
i
th
m
s
iter
ativ
ely
esti
m
ate
th
e
f
ilter
weig
h
ts
(
)
=
[
0
(
)
,
1
(
)
,
…
,
(
−
1
)
(
)
]
(
2
)
T
h
e
Gain
m
atr
ix
(
)
is
ex
p
lain
ed
in
(
3
)
,
(
)
=
(
0
(
)
,
1
(
)
,
…
.
,
−
1
(
)
)
(
3
)
an
d
g
ain
f
ac
to
r
(
)
is
as
s
ig
n
ed
to
th
e
i
th
tap
in
p
r
o
p
o
r
tio
n
t
o
|
(
)
|
(
)
=
(
)
1
∑
(
)
=
0
−
1
(
4
)
Fo
r
th
e
s
im
p
lifie
d
PLM
S a
lg
o
r
ith
m
,
(
)
f
o
r
ea
ch
ta
p
is
ev
alu
ate
d
as
(
)
=
[
|
(
)
|
+
]
(
5
)
an
d
[
|
(
)
|
]
=
|
(
)
|
(
6
)
T
h
e
Pt
-
LMS
alg
o
r
ith
m
s
im
p
lifie
s
its
p
r
ed
ec
ess
o
r
s
b
y
o
m
itti
n
g
weig
h
ted
n
o
r
m
aliza
tio
n
a
n
d
s
im
p
lify
in
g
g
ain
f
ac
to
r
e
v
alu
atio
n
,
with
a
s
m
all
co
n
s
tan
t
p
en
s
u
r
in
g
m
i
n
im
u
m
g
ain
f
o
r
i
n
ac
tiv
e
c
o
ef
f
icien
t
s
an
d
r
ed
u
ci
n
g
tim
e
co
m
p
lex
ity
.
T
h
ese
ch
an
g
es
im
p
r
o
v
e
ar
ea
an
d
p
o
wer
ef
f
ic
ien
cy
,
b
u
t
h
ig
h
tim
e
co
m
p
lex
ity
r
em
ain
s
d
u
e
t
o
r
ep
ea
ted
g
ain
m
atr
ix
an
d
wei
g
h
t
u
p
d
ates.
Dela
y
e
d
a
d
ap
tati
o
n
a
d
d
r
ess
es
th
is
is
s
u
e,
lev
er
a
g
in
g
t
h
e
u
n
ch
a
n
g
ed
er
r
o
r
g
r
ad
ien
t
d
esp
ite
d
elay
s
.
(
+
1
)
=
(
)
+
(
−
)
(
−
)
(
−
)
(
7
)
W
h
en
we
co
m
p
ar
e
th
e
r
esu
lts
o
f
Pt
-
L
MS
wi
th
o
th
er
L
MS
alg
o
r
ith
m
we
o
b
s
er
v
e
th
at
th
e
co
n
v
er
g
en
c
e
p
er
f
o
r
m
an
ce
o
f
Pt
-
LMS
is
c
o
m
p
ar
ativ
ely
b
etter
th
an
th
at
o
f
o
th
e
r
L
MS
al
g
o
r
ith
m
s
a
n
d
its
co
n
v
er
g
en
ce
p
er
f
o
r
m
an
ce
ca
n
b
e
im
p
r
o
v
e
d
f
u
r
th
er
.
I
t
is
also
o
b
s
er
v
ed
th
at
Pt
-
L
MS
i
s
r
ea
l
t
im
e
f
lex
ib
le
an
d
r
o
b
u
s
t.
Hen
ce
,
we
d
ec
id
ed
to
m
o
v
e
f
o
r
war
d
with
PLM
S.
3.
ARCH
I
T
E
C
T
UR
E
3
.
1
.
P
ro
po
s
ed
P
L
M
S a
rc
hite
ct
ur
e
Fig
u
r
e
2
s
h
o
ws
th
e
p
r
o
p
o
s
ed
PLM
S
ar
ch
itectu
r
e
an
d
f
l
o
atin
g
-
p
o
i
n
t
ad
d
e
r
b
l
o
ck
r
esp
ec
tiv
ely
.
As
illu
s
tr
ated
in
Fig
u
r
e
2
(
a)
;
to
im
p
lem
en
t
p
ip
elin
in
g
,
th
e
n
u
m
b
er
o
f
tap
s
in
cr
ea
s
es
with
t
h
e
o
r
d
er
o
f
th
e
f
ilter
,
wh
ich
s
ig
n
if
ican
tly
im
p
ac
ts
th
e
ar
ea
.
Ad
d
itio
n
ally
,
s
witch
es
ar
e
p
lace
d
af
ter
ev
er
y
two
tap
s
to
m
an
ag
e
th
e
tap
-
o
u
t
an
d
g
am
m
a
f
u
n
ctio
n
at
th
e
co
r
r
esp
o
n
d
in
g
cl
o
ck
p
h
ases
.
W
h
ile
th
ese
s
witch
es
h
elp
r
ed
u
c
e
tim
in
g
co
m
p
lex
ity
,
th
eir
lar
g
e
n
u
m
b
er
co
n
tr
ib
u
tes
to
in
cr
ea
s
ed
ar
ea
.
I
n
s
tead
o
f
co
n
n
ec
tin
g
th
e
r
eg
r
ess
o
r
in
p
u
t
an
d
in
itial we
ig
h
ts
d
ir
ec
tly
to
th
e
t
ap
s
,
th
ey
ar
e
r
o
u
ted
th
r
o
u
g
h
a
s
witch
.
Dep
en
d
in
g
o
n
th
e
clo
c
k
p
h
ase,
ea
ch
f
ilter
co
ef
f
icien
t
a
n
d
i
n
p
u
t
p
ass
th
r
o
u
g
h
a
f
lo
atin
g
-
p
o
in
t
m
u
ltip
li
er
,
wh
ich
ac
ce
ler
ates
th
e
m
u
l
tip
licatio
n
p
r
o
c
ess
an
d
g
e
n
er
ates
th
e
tap
-
out
(
n
)
an
d
g
ain
f
u
n
ctio
n
.
T
h
ese
o
u
tp
u
ts
ar
e
d
ir
ec
ted
t
o
s
witch
2
.
Af
ter
all
th
e
o
u
t
p
u
ts
ar
e
g
en
er
ated
f
r
o
m
a
s
in
g
le
ta
p
,
s
witch
1
is
ac
tiv
ated
d
u
r
in
g
o
n
e
clo
ck
p
h
ase
an
d
s
witch
2
in
th
e
n
ex
t.
W
h
en
s
witch
1
is
ac
tiv
e,
th
e
ad
d
er
s
u
m
s
th
e
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I
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s
ig
n
s
.
T
h
e
s
ec
o
n
d
m
an
tis
s
a
is
s
u
b
tr
ac
ted
f
r
o
m
2
4
′
ℎ
8
0
0
0
0
0
2
4
an
d
th
e
r
esu
lt
is
m
u
ltip
lied
with
th
e
f
ir
s
t
m
an
tis
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a
u
s
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g
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Ved
ic
m
u
ltip
lier
.
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h
e
o
u
tp
u
ts
ar
e
ad
d
e
d
to
f
o
r
m
th
e
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lt'
s
m
an
tis
s
a,
wh
ile
th
e
ca
r
r
y
is
ad
d
ed
to
th
e
ex
p
o
n
en
t
b
lo
ck
f
o
r
t
h
e
f
in
al
r
esu
lt'
s
ex
p
o
n
en
t.
(
a)
(
b
)
Fig
u
r
e
5
.
Key
co
m
p
u
tatio
n
al
b
lo
ck
s
o
f
th
e
f
lo
atin
g
-
p
o
in
t
u
n
it
: (
a)
Flo
atin
g
-
p
o
in
t
m
u
ltip
lier
f
o
r
ef
f
icie
n
t
m
u
ltip
licatio
n
o
p
e
r
atio
n
s
an
d
(
b
)
Flo
atin
g
-
p
o
in
t
d
iv
id
er
f
o
r
p
r
ec
is
e
d
iv
is
io
n
co
m
p
u
tatio
n
s
4.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
4
.
1
.
M
AT
L
A
B
re
s
u
lt
s
T
h
is
s
y
s
tem
was
b
u
ilt
to
an
aly
ze
an
y
t
y
p
e
o
f
in
p
u
t
s
ig
n
al
wh
ich
is
g
en
er
ated
b
y
a
s
ig
n
a
l
g
en
er
ato
r
,
th
e
s
am
e
s
ig
n
al
is
b
ee
n
p
ass
ed
to
u
n
k
n
o
wn
s
y
s
tem
an
d
a
d
ap
tiv
e
s
y
s
tem
an
d
r
esu
lts
in
er
r
o
r
co
n
v
er
g
en
c
e
ef
f
ec
tiv
ely
.
On
ce
we
ac
h
iev
e
d
s
atis
f
ac
to
r
y
r
esu
lts
,
we
s
tar
ted
th
e
s
y
n
th
esis
.
T
h
e
Simu
lin
k
d
ia
g
r
am
ca
n
b
e
s
ee
n
in
Fig
u
r
e
6
(
a)
.
Fig
u
r
e
6
(
b
)
p
r
esen
ts
th
e
Simu
lin
k
m
o
d
el
r
esu
lts
f
o
r
th
e
ad
ap
tiv
e
f
ilter
,
s
h
o
win
g
th
e
d
esire
d
s
ig
n
al,
th
e
f
ilter
'
s
o
u
tp
u
t,
an
d
t
h
e
er
r
o
r
s
ig
n
al.
T
h
e
th
ir
d
g
r
a
p
h
d
em
o
n
s
tr
ates
er
r
o
r
co
n
v
e
r
g
en
ce
o
v
er
tim
e.
Fig
u
r
e
6
(
c)
s
h
o
ws
th
e
r
eg
is
ter
tr
an
s
f
er
lan
g
u
ag
e
(
R
T
L
)
s
ch
em
atic
o
f
PLM
S
alg
o
r
ith
m
u
s
in
g
L
ib
er
o
SOC
v
er
s
io
n
1
1
.
9
with
FP
GA
A3
P1
0
0
0
L
f
r
o
m
Pro
ASI
C
3
L
s
er
ies.
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:
2088
-
8
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E
fficien
t p
o
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er o
p
timiz
ed
ve
r
y
-
la
r
g
e
-
s
ca
le
in
teg
r
a
tio
n
…
(
Ga
n
g
a
d
h
a
r
a
ia
h
S
o
r
a
la
m
a
vu
L
a
ksh
ma
ia
h
)
2519
(
a)
(
b
)
(
c)
Fig
u
r
e
6
.
C
o
m
p
r
eh
en
s
iv
e
a
n
al
y
s
is
an
d
im
p
lem
en
tatio
n
o
f
th
e
p
r
o
p
o
s
ed
PLM
S a
r
ch
itectu
r
e
: (
a)
Simu
lin
k
m
o
d
el
o
f
th
e
p
r
o
p
o
s
ed
PLM
S a
r
ch
itectu
r
e,
(
b
)
Simu
lin
k
r
esu
lt f
o
r
s
in
e
wav
e
d
e
m
o
n
s
tr
atin
g
th
e
s
y
s
tem
r
esp
o
n
s
e,
an
d
(
c)
R
T
L
s
ch
em
a
tic
r
ep
r
esen
tin
g
th
e
h
ar
d
war
e
im
p
lem
en
tatio
n
o
f
th
e
PLM
S a
lg
o
r
ith
m
4
.
2
.
Sy
nthesis
re
s
ults
T
ab
le
s
2
an
d
3
r
ep
r
esen
ts
th
e
ch
ar
ac
ter
is
tics
o
f
p
r
o
p
o
s
e
d
PLM
S
an
d
co
m
p
ar
is
o
n
with
ex
is
tin
g
ar
ch
itectu
r
es
r
esp
ec
tiv
ely
.
Fig
u
r
es
7
(
a)
an
d
7
(
b
)
s
h
o
w
th
e
g
r
ap
h
ical
co
m
p
ar
is
o
n
p
o
wer
-
d
el
ay
co
m
p
ar
is
o
n
an
d
tim
in
g
co
m
p
ar
is
o
n
with
r
ef
e
r
r
ed
alg
o
r
ith
m
s
with
ex
is
tin
g
DL
MS
an
d
DW
MPL
M
S
ar
ch
itectu
r
es
r
esp
ec
tiv
ely
.
T
ab
le
3
s
h
o
ws
th
at
th
e
p
r
o
p
o
s
ed
d
esig
n
r
ed
u
ce
s
p
o
wer
co
n
s
u
m
p
tio
n
b
y
u
p
to
9
5
%,
with
s
av
in
g
s
o
f
9
5
%
an
d
8
8
%
co
m
p
ar
e
d
to
th
e
DW
MPL
MS
an
d
DM
PLM
S
ar
ch
ite
ctu
r
es,
r
esp
ec
tiv
ely
.
T
h
is
ef
f
i
cien
cy
s
tem
s
f
r
o
m
r
ep
lacin
g
th
e
l
o
g
ar
ith
m
ic
m
u
lt
ip
lier
with
a
f
lo
atin
g
-
p
o
in
t
Ve
d
ic
m
u
ltip
lier
,
wh
ich
en
h
a
n
ce
s
p
o
wer
ef
f
icien
c
y
.
T
h
e
p
ip
elin
e
d
tap
b
lo
ck
d
esi
g
n
f
u
r
th
er
im
p
r
o
v
es
tim
in
g
,
m
ak
in
g
t
h
e
p
r
o
p
o
s
ed
ar
ch
itec
tu
r
e
3
0
tim
es
f
aster
th
an
lo
g
ar
ith
m
ic
m
eth
o
d
s
an
d
th
e
DL
MS
d
esig
n
.
Fix
ed
-
p
o
i
n
t
co
m
p
u
tatio
n
s
in
DL
MS
ar
e
less
ef
f
icien
t,
with
th
e
p
r
o
p
o
s
ed
f
lo
atin
g
-
p
o
in
t b
l
o
ck
s
ac
h
iev
in
g
8
4
% p
o
wer
s
av
in
g
s
.
T
ab
le
2
.
C
h
ar
ac
ter
is
tics
o
f
p
r
o
p
o
s
ed
PLM
S
A
r
c
h
i
t
e
c
t
u
r
e
P
o
w
e
r
(
mW
)
D
e
l
a
y
(
n
s)
D
W
M
P
L
M
S
(
M
u
l
a
e
t
a
l
.
[
2
]
)
1
9
.
4
3
3
.
3
1
D
LM
S
(
M
e
h
e
r
a
n
d
P
a
r
k
[
1
]
)
1
0
.
0
6
3
.
2
8
D
LM
S
(
F
a
n
e
t
a
l
.
[
2
1
]
)
1
2
.
5
6
3
.
2
7
D
M
P
LM
S
[
2
]
1
4
.
1
3
3
.
3
1
P
r
o
p
o
se
d
(
P
L
M
S
)
1
.
0
6
7
10
4
.
4
5
T
ab
le
3
.
C
o
m
p
a
r
is
o
n
with
ex
i
s
tin
g
ar
ch
itectu
r
es
M
e
t
r
i
c
V
a
l
u
e
N
u
mb
e
r
o
f
sl
i
c
e
s
LU
T's
u
s
e
d
4
0
9
4
N
u
mb
e
r
o
f
I
O
c
e
l
l
s
u
se
d
1
6
1
D
e
v
i
c
e
p
o
w
e
r
c
o
n
s
u
m
p
t
i
o
n
1
.
0
6
7
mW
O
p
e
r
a
t
i
n
g
f
r
e
q
u
e
n
c
y
/
t
i
mi
n
g
9
.
5
M
H
z
O
p
e
r
a
t
i
n
g
t
i
mi
n
g
1
0
4
ns
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
15
,
No
.
2
,
Ap
r
il
20
25
:
2
5
1
3
-
2
5
2
2
2520
(
a)
(
b
)
Fig
u
r
e
7
.
T
h
e
g
r
a
p
h
ical
:
(
a)
p
o
wer
co
m
p
ar
is
o
n
an
d
(
b
)
tim
in
g
co
m
p
a
r
is
o
n
,
with
r
e
f
er
r
ed
alg
o
r
ith
m
s
5.
CO
NCLU
SI
O
N
T
h
is
p
ap
er
p
r
o
p
o
s
es a
n
ad
ap
tiv
e
f
ilter
d
esig
n
th
at
lev
er
ag
es th
e
PLM
S a
lg
o
r
ith
m
,
wh
ich
is
ap
p
lied
to
a
3
2
-
b
it
f
ilter
len
g
th
.
T
h
e
PLM
S
alg
o
r
ith
m
is
s
elec
ted
d
u
e
to
its
ab
ilit
y
to
ac
h
iev
e
a
lo
wer
m
ea
n
-
s
q
u
ar
e
-
d
ev
iatio
n
(
MSD)
c
o
m
p
a
r
ed
to
th
e
tr
ad
itio
n
al
L
MS
alg
o
r
ith
m
,
r
esu
ltin
g
i
n
b
etter
p
er
f
o
r
m
an
ce
.
Ad
d
itio
n
ally
,
PLM
S
o
f
f
er
s
f
aster
co
n
v
er
g
e
n
ce
th
an
th
e
DL
MS
alg
o
r
ith
m
,
m
ak
i
n
g
it
a
m
o
r
e
ef
f
icien
t
ch
o
ice
in
ter
m
s
o
f
ar
ea
,
p
o
wer
,
an
d
tim
in
g
.
T
h
e
p
r
o
p
o
s
ed
d
esig
n
r
ep
lace
s
th
e
lo
g
ar
ith
m
ic
ap
p
r
o
ac
h
i
n
ex
is
tin
g
DW
MPL
MS
an
d
DM
PLM
S
ar
ch
itectu
r
es
with
f
lo
atin
g
-
p
o
in
t
co
m
p
u
tatio
n
,
a
Ved
ic
m
u
ltip
lier
,
an
d
a
p
r
o
p
o
r
tio
n
ate
g
ain
b
lo
ck
.
A
p
i
p
elin
e
d
ar
ch
itectu
r
e
in
th
e
tap
b
lo
c
k
en
h
an
ce
s
ef
f
icien
cy
,
wh
ile
th
e
d
esig
n
in
clu
d
es
ap
p
r
o
x
im
ate
m
u
ltip
lier
s
,
f
lo
atin
g
-
p
o
in
t
ad
d
e
r
s
,
an
d
d
iv
i
d
er
b
l
o
ck
s
.
FP
GA
s
y
n
th
esis
s
h
o
ws
a
9
2
%
p
o
wer
r
ed
u
ctio
n
co
m
p
ar
ed
to
e
x
is
tin
g
ar
ch
itectu
r
es.
Fu
tu
r
e
wo
r
k
f
o
c
u
s
es o
n
r
ed
u
ci
n
g
ar
ea
,
im
p
r
o
v
in
g
tim
in
g
,
a
n
d
f
i
n
e
-
tu
n
i
n
g
o
u
tp
u
t p
er
f
o
r
m
an
ce
.
RE
F
E
R
E
NC
E
S
[
1
]
P
.
K
.
M
e
h
e
r
a
n
d
S
.
Y
.
P
a
r
k
,
“
A
r
e
a
-
d
e
l
a
y
-
p
o
w
e
r
e
f
f
i
c
i
e
n
t
f
i
x
e
d
-
p
o
i
n
t
L
M
S
a
d
a
p
t
i
v
e
f
i
l
t
e
r
w
i
t
h
l
o
w
a
d
a
p
t
a
t
i
o
n
-
d
e
l
a
y
,
”
I
E
EE
T
ra
n
s
a
c
t
i
o
n
s
o
n
V
e
ry
L
a
rg
e
S
c
a
l
e
I
n
t
e
g
ra
t
i
o
n
(
VLS
I
)
S
y
st
e
m
s
,
v
o
l
.
2
2
,
n
o
.
2
,
p
p
.
3
6
2
–
3
7
1
,
F
e
b
.
2
0
1
4
,
d
o
i
:
1
0
.
1
1
0
9
/
TV
LSI
.
2
0
1
3
.
2
2
3
9
3
2
1
.
[
2
]
S
.
M
u
l
a
,
V
.
C
.
G
o
g
i
n
e
n
i
,
a
n
d
A
.
S
.
D
h
a
r
,
“
A
l
g
o
r
i
t
h
m
a
n
d
V
LSI
a
r
c
h
i
t
e
c
t
u
r
e
d
e
s
i
g
n
o
f
p
r
o
p
o
r
t
i
o
n
a
t
e
-
t
y
p
e
LM
S
a
d
a
p
t
i
v
e
f
i
l
t
e
r
s
f
o
r
sp
a
r
se
sy
s
t
e
m
i
d
e
n
t
i
f
i
c
a
t
i
o
n
,
”
I
E
EE
T
ra
n
s
a
c
t
i
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c
a
n
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Evaluation Warning : The document was created with Spire.PDF for Python.