Syn he ic Gene a ion o Add ess-E en s o Real-Time Image P ocessing
A. Lina es-Ba anco,
R.
Senhadji-Na a o,
I.
Ga cia-Va gas,
F.
Gomez-Rod iguez,
G.
Jimenez
and
A.
Ci i .
A qui ec u a y Tecnologia de Compu ado es.
Uni e sidad de Se illa.
AV. Reina Me cedes
sin,
41012-Se illa
SPAIN
Absnac
-
Add ess-E en -Rep esen a ion
(AER)
is
a s eam. The ecei e pixel in eg a es he pulses and
commnnica lon p o ocol ha emula es he nenons sys em’s econs mc s he o iginal low equency COn inUOUS- ime
neu ons communica ion, and ha
is
ypically used o wa e o m, like
a
neu on does in he ne ous sys em. Pixels
ans e ing Images be ween chips.
I
was o iginally de eloped ha a e mo e ac i e a e accessing he bus mo e equen ly
o bio-inspi ed and eal- ime image p ocessing sys ems. Such han hose
less
ac , e.
sys ems may consis
o
a complica ed hie a chical s nc n e
~~~~~~i~~i~~
he add esses
pe o ming
exba
ope a ions
on
he images while hey a el om one chip o
wi h many chips ha ansmi Images among hem in eal ime,
while pe o ming some p ocessing ( o example, con olu ions).
s eams om images s o ed
in
a compu e ’s
memo y
a e
(le. EEPROM) allows ans o ma ion (ie. shi ing and
p esen ed.
A
ha dwa e e sion
will
wo k
in
eal- ime.
~11
o
o a ion) o images. Also, he image ansmi ed by one chip
hem ha e been e alua ed and compa ed. can be ecei ed by many ecei e chips
in
pa allel, by
p ope ly handling he asynch onous communica ion
p o ocol. The peculia na u e o he AER p o ocol
also
1.
INTRODUCTION allows o e y e icien con olu ion ope a ions wi hin a
1991
by Si ilo i
[SI
o ans e ing he s a e o an a ay o The e
is
a g owing communi y o AER p o ocol use s o
analog
ime dependan alues om one chip o ano he . I bio-inspi ed applica ions in ision and audi ion sys ems,
as
uses mixed analog and digi al p inciples and exploi s pulse demons a ed by he success
in
he las yea s o he AER
densi y modula ion o coding in o ma ion. Fig.
1
explains g oup
a
he Neu omo phic Enginee ing Wo kshop se ies
he p inciple behind he AER basics.
[I].
The
goal
o his communi y is o build la ge mul i-chip
and mul i-laye hie a chically s uc u ed sys ems capable o
pe o ming complica ed a ay da a p ocessing in eal ime.
The success
o
such sys ems
will
s ongly depend
on
he
a ailabili y o obus and e icien de elopmen and
debugging AER- ools. One such ool is
a
compu e in e ace
ha allows no only eading an AER s eam in o a compu e
and displaying
i
on
i s sc een
in
eal- ime, bu
also
he
opposi e: om images a ailable
in
he compu e ’s memo y,
In
his pape se e a] so wa e me hods
o
gene a ing
AER
ano he ’
Fo
p ope ly
coded
memo ies
Add ess-E en -Rep esen a ion (AER) was p oposed in ecei e chip
[81.
[U
’
gene a e
a
syn he ic AER s eam in a simila manne
as
would do
a
dedica ed VLSI AER emi e chiu
121141151161.
Fig.
1:
lllus mlion
o
AER
in ci-chip
communic ion
scheme
The Emi e chip con ains
an
a ay o cells (like, o
example,
a
came a o a i icial e ina chip) whe e each pixel
shows a con inuously a ying ime dependan s a e ha
changes wi h
a
slow ime cons an (in he magni ude o de
o milliseconds). Each cell o pixel includes
a
local
oscilla o (VCO) ha gene a es digi al pulses o minimum
wid h
(a
ew nanoseconds). The densi y
o
pulses is
p opo ional o he s a e o he pixel (o pixel in ensi y).
Each ime a pixel gene a es a pulse (which is called
“e en ”), i communica es o he a ay pe iphe y and a
digi al wo d ep esen ing
a
code
o
add ess o ha pixel is
placed on he ex e nal in e -chip digi al bus ( he AER bus).
Addi ional handshaking lines (Acknowledge and Reques )
a e
also
used o comple ing he asynch onous
1
.
_I
..
..
~
This wo k
is
in ol e
in
he amewo k
o
he Eu opean
Resea ch p ojec CAVIAR, one o he objec i es o
CAVIAR is de elop
an
AER-compu e in e ace. This pape
p esen s se e al me hods o syn he ic AER s eams
gene a ion (sec ion
11).
I also e alua es hese me hods
a ending o he execu ion ime compa ison and o he e o
o dis ibu ion o he e en s along he ime associa ed o an
image: dis ance o he equency
o
an
in ensi y le el. The
me hods a e e alua ed o images wi h di e en a e age
in ensi y le els wi h
a
Gaussian his og am ( om
IO-90%
cha ge o e en s) and o
a
ypical image (a ound
50
%
cha ge o e en s) (sec ion
111).
Conclusions p esen ed
in
his
pape a e no de ini i e. A ha dwa e implemen a ion and i s
s udy is necessa y o a eal- ime compa ison.
communica ion. The in e -chip AER bus ope a es a he
maximum possible speed.
In
he ecei e chip he pulses a e
11.
SYNTHETIC STREAM GENERATION
di ec ed o he pixels o cells whose code o add ess was
on
he bus. This way, pixels wi h he same code o add ess in
he
emi e
and
ecei e
chips
The e
a e
many
so wa e
o
ans o m
a
he
Same
pulse
bi map image in o an AER s eam o pixel add esses.
In
all
0-7803-7937-3/03/$17.00
02003
IEEE
462
o hem he equency
o
appea ance o he add ess o
a
This me hod can be enhanced by applying
a
small shi
gi en pixel mus be p opo ional o he in ensi y o ha be o e placing he e en s in he ime pe iod. This shi can
pixel. No e ha he p ecise loca ion o he add ess pulses
is
he he add ess o he pixel. Fo example, he pixel
no c i ical. The pulses can be sligh ly shi ed om hei (i,j)=(lO,lO) will be shi ed
1290
posi ions espec o he
nominal posi ions; he AER ecei e s will in eg a e hem o beginning o he ime pe iod
(N=M=I28).
These posi ions
eco e he o iginal pixel wa e o m. The ha dwa e implemen a ion o his me hod is unde
add esses ha will he sen o
an
AER ecei e chip ia an s udy. Bul, he ime pe iod
is
no gene a ed sequen ially,
so,
AER bus. I we ha e an image o
NxM
pixels and each pixel he i s implemen a ion seems o need a memo y o sa e he
can ha e
a
g ey le el alue om
0
o
K,
one possibili y
is
o comple e ime pe iod be o e ansmi ing
i .
place each pixel add ess in he add ess sequence as many
imes as he alue o
i s
in ensi y, and dis ibu ed in
D.
TheRandom me hod
a e ansla ed as a delay in he ansmission
o
he e en .
Wha e e algo i hm
is
used, i will gene a e a ec o o
equidis an posi ion.
In
he wo s case (all pixels wi h alue This me hod places he add ess e en s in he slo
e,
he add ess sequence would be illed wi h
NxMxK
posi ions ob ained by a pseudo- andom numbe gene a o
based
on
Linea Feedback Shi Regis e s (LFSR)
[71[9].
add esses. The ime used o ansmi ing an image needs o
be he same o any image in ensi y cha ge, lea ing blank Due o he p ope ies o he LFSR used, each slo posi ion is
slo s i non-e en has o he sen . Each algo i hm would gene a ed only once and
no
collisions appea . I a pixel in
implemen a pa icula way o dis ibu ing hese add ess he image has he in ensi y
p,
hen he me hod will ake
p
e en s. Le
us
p opose some algo i hms: he Scan me hod,
he Scan Slice, he Uni o m me hod, he Random me hod,
alues
om
he
pseudo- andom
numbe
gene a o
and
places he pixel add ess in he co esponding
p
slo s o he
he Random-Squa e me hod and he Exhaus i e me hod. add ess sequence. They will no be equidis an bu will
appea along he comple e add ess sequence. This me hod
is
as , because he image is swep only once, and because he
In
his
me hod he image
is
scanned many imes. Fo each algo i hm does no need o pe o m sea ches o emp y Slo s.
scan, e e y ime
a
non-ze o pixel is eached i s add ess is Due o he LFSR can ob ain
wo
consecu i e, o e y
pu
on
he add ess sequence in he i s a ailable slo , and close , add esses in a ew calls, The LFSR-based me hod
he pixel alue
is
dec emen ed by one. This me hod is e y can be enhanced using
a
b-bi coun e o he high pa
O
as , due o
i
does no need o look o emp y slo s, al hough he add ess.
So,
o each call o he add ess gene a o ,
2'
he image needs o be scanned many imes
(K
imes
in
he add esses equally dis ibu ed a e ob ained. Fo pixels wi h
a
wo s case). Howe e , a non-well e en dis ibu ion
is
g ey le el alue nea o
2',
he dis ibu ion ob ained
is
ob ained due o all pulses o he pixels wi h low in ensi y simila o he ideal dis ibu ion. Howe e , o di e en g ey
alues will appea only a he beginning o he sequence. le el, he dis ibu ion o he e en s ge s wo se again. Tuning
he co ec size coun e alue o each image, he andom
B.
The Scan-Slice me hod
me hod could be imp o ed.
A.
The Scan me hod
Fig.
2
shows he LFSR s mc ll e wi h a 2-bi coun e o a
128x128 images wi h a
256
g ey le els.
The Scan me hod can be enhanced in he dis ibu ion
o
e en s i an blank slo
is
le when
all
he e en s o a pixel
LSB
.>21
ha e been sen . The image is scanned many imes, as he
h,SB
L
FIR
Scan me hod does, hu when a pixel's alue is ze o,
a
blank
" -1
'7
''1
''1 "1
7
1
'1
7
7
I
I
I
I
y
slo
IS
le in he ime pe iod.
---
I
I
-
Bo h he Scan me hod and he Scan-Slice me hod can be
easily implemen ed in ha dwa e, because he ime pe iod is
gene a ed sequen ially.
So,
he equi ed ha dwa e could be a
memo y o s o e he image, plus he con ol ci cui y
C.
The UniJo m nielhod
Fig.
2:
LFSR
wi h
a
2-bi
coun e
o pseudo- andom
numbe s
gene a ion.
E.
The Randoni-Squa e me hod
Using he Random me hod wi h a ixed size coun e om
In
his me hod, he image is scanned pixel by pixel only
I
o he maximum g ey le el, he e en dis ibu ion o high
once. Fo each pixel, he gene a ed pulses mus be le el pixels is accep able, bu poo o low le el alues.
dis ibu ed a equal dis ances.
As
he sequence
is
ge ing Subs i u ing he coun e by ano he LFSR, he dis ibu ion
illed, he algo i hm may wan o place add esses in slo s could he imp o ed.
ha a e al eady occupied. This si ua ion
is
called 'collision' Fo a 128x128 image wi h maximum g ey le el o
255,
and
i
appea s wi h he AER app oxima ion o in e -chip 8-bi LFSR (LFSR-8) is used o selec ing
255
slices o
communica ion.
In
his case,
i
will pu he pulse in he 128x128 posi ions. and ano he 14-bi LFSR (LFSR-14)
nea es emp y slo o he ime pe iod.
So,
his me hod, selec s he posi ion inside he slice. The image
is
scanned
appa en ly, will make mo e mis akes a he end o he only once. Fo each pixel a 14-bi numbe
is
gene a ed by
p ocess han he beginning. The execu ion ime g ows he LFSR-14, and he LFSR-8 is called as many imes as he
conside ably because he collisions ha e
a
high-cos in ime in ensi y le el
o
he pixel would indica e. Fig.
3
shows he
o esol e i . LFSRs used by he Random-Squa e me hod.
463
mlm7
h,SB
LNR-8
“R-I,
LSB
Fig.
3:
LFSR-8 and LFSR-I4
used
by
he
Random-Squa e me hod.
F.
The
Exhaus i e me hod
This algo i hm also di ides he add ess e en sequence in
K
slices o
NxM
posi ions o an image o
NxM
pixels wi h a
maximum g ey le el o
K.
This me hod is based
on
he idea
ha each possible e en (as maximum
NxMxK)
has assigned
one ixed posi ion in he add ess e en sequence.
In
such
way, o he slice
k,
an
e en o he pixel
(i,j)
is sen
on
he
ime
i he ollowing condi ion is asse ed:
(k.
c.,j)mod
K
+
c,j
2
K
N,
M
.(k
-l)+
(i
-
I)
.M
+
j
=
I
whe e
c,j
is he in ensi y alue o he pixel
0,j)
(see Fig.
4).
and
N.M
K
Fig.
5:
Tes
image
se
(10%
o
90%
e en
cha ge)
All o he me hods ha e been implemen ed in
C++
language and an applica ion has been de eloped which
allows he gene a ion
o
he add ess e en s om
a
g ey-
scale image and calcula e he pa ame e s p e iously
desc ibed. The Fig.
6
shows he sc eensho
o
his so wa e
in e ace.
Fig.
4
The e en dis ibu ion made
by
he Exhaus i e
mu had.
The Exhaus i e me hod ies o dis ibu e he e en s
o
each pixel in o he
K
slices a equal dis ances.
Fo
his
p opose, he algo i hm scan he image
K
imes.
In
he
i e a ion
k,
i he p e ious condi ion is ue, hen he
co esponding e en is sen , o he wise he algo i hm will
wai
o
he
ollowing
e en
(no
e en
is sen
in
ime
).
Ill.
SIMULATION RESULTS
This sec ion is de o ed o compa e he me hods p oposed
abo e and o es ima e how he pe o mance o he me hods
is a ec ed by he ype o image. To ca y ou his analysis a
se
o
andom images we e gene a ed, which ep esen a
small popula ion
o
images.
In
o he s popula ion
o
images,
wi h di e en his og am pa e ns
and
cha ge o e en s, he
esul s can a y in some me hods, al hough he beha iou
A,
will be close o he esul s he e p esen ed.
Theses images was ob ained conside ing wo aspec s: i s The in e es o his poin is no o compa e he execu ion
his og am mus be close o Gaussian dis ibu ion and he ime
o
each me hod, because he me hods ha e o be
numbe o e en s needed o ansmi hem, his is called implemen ed in ha dwa e o a mo e eal compa ison.
“image e en cha ge” and his way.
100%
e en cha ge The e o e he in e es yields
in
he beha iou o he
co esponds o an image wi h all
o
pixel wi h inaximum execu ion ime o each me hod espec o he image e en
alue.
In
such a way,
a
image wi h
10%
o
e en cha ge, cha ge. The es has been made by using he nine images
ep esen an image
ha
used
10%
o he possible e en . Fig. showed
in
Fig.
5.
5
shows he images used.
Fig.
6
So wa e
in e ace
The
execu ion
464
(
p j)
and he ollowing e en
(
p$'
).
The dis ance o he
las e en
is
calcula ed supposing ha he nex e en
is
he
i s o
a
new sequence o he same image (we suppose ha
he image
is
con inuously e-sen ).
i.
Then we can measu e he mean e o o
a
pixel
as
he
a e age o he di e ences be ween he ideal and eal
dis ance. The e o exp ession is:
gIQ.,
-d&
e
,
=
'.I
k=I
G.i
I
is
easy
o
see ha he wo s case o his e o
measu emen is which
all
he e en s a e oge he
in
he
add ess sequence (see Fig.
8).
The e o e,
in
o de o compa e he e o ob ained o
di e en me hods and images, he e o o each pixel mus
be no malized espec
o
he maximum e o associa ed o
he pixel. The ollowing exp ession
is
he maximum e o
--.
I
).(I--)
wi h
e.,j#l
4.j
Scan
Slice
and
Exhaus i e
me hod.
Fo
y,,
=
1,
he dis ibu ion e o
is
ze o, due o only one
Fig.
7
shows he execu ion ime e sus he cha ge o
e en
has
o
be
sen .
e en s
in
he image. Scan and Exhaus i e me hods ollow
an
almos cons an ela ion because he cha ge o e en s is
aduced in o
an
insigni ican inc easing in ime execu ion,
al hough his a ec o he Exhaus i e me hod
in
images wi h
hal cha ge o e en s. Random, Random Squa e and Scan
Slice me hods ollow
a
g owing up beha iou , due o he
inc easing numbe o e en s, wi h
a
less e ec
in
he Scan
Slice. Uni o m me hod
is
also
a ec ed h collisions (c ows
Fig.
8:
The
wo s
e en
dis ibu ion.
_-
up as e han o he s), hu
i
seems
o
be a ela ion be ween
he numbe o collisions and he his og am,
as
can be seen
compa ing heses esul s wi h he ypical image. Finally, we de ine
a
ma ix wi h he same dimensions
O
he image, whe e he
@,j)
elemen ep esen s he e o
no malized o he pixel
@,j):
B.
The
dis ibu ion
e o
In
an ideal AER dis ibu ion
all
e en s o one pixel and
image could be equidis an in ime: cons an equency o
e en s.
In
his sec ion, he dis ibu ion o e en ob ained
wi h each me hod
is
e alua ed. The dis ibu ion e o
p oposed measu es how much he e en dis ibu ion
ene a ed by a me hod de ia es om he ideal dis ibu ion.
NE=
-
Le suppose
Du
is
he ideal dis ance be ween e en s o he Fig.
9
shows he no malized dis ibu ion e o calcula ed
o he nine es images using he me hods p oposed. The
x-
axis ep esen s he image e en cha ge and he y-axis is he
e o .
pixel
0,j)
o a
NxM
image wi h
K
g ey le el alues.
N.WK
D.
.=-
4.j
'.J
whe e
4.,
is he in ensi y alue
o
he pixel
@,j).
465
Oi5 nbu18on
e o
VE
wen
cha ge
+
Scan-slice
8
Random
4
Random-Squa e
B
Exhaus i e
06
02
01
Fig.
I
I:
Typical
image
his og am
o
20
30
40
y1
60
70
00
90
Image
wen
cha ge
(%)
Fig.
9
Dis ibu ion
e o
Me hod
Mean
S d.
De~ls lon
69.75 17.14 99.22
49.19 35.58 99.22
84.95
14.69 98.43
17.25
Uni o m
Random
(2bi s)
34.92
As
i
can he seen, he minimum e o is ob ained wi h he
The e o dec eases wi h he image e en cha ge
io
all
exhaus i e me hod.
me hods excep Random.
In
he scan me hods he pixels
wi h low in ensi y has a highe e o o dis ibu ion inside
he ime pe iod selec ed o ansmi! ing he image,
The Uni o m me hod has he bes esul s
o
he
popula ion o image selec ed, because he e a e e y ew
In
his pape six so wa e me hods o gene a ing AER
collisions. This me hod ep esen he close solu ion o he s eams om images s o ed in a compu e 's memoly ha e
ideal dis ibu ion in he con ex p esen ed. been p esen ed. The di e en me hods ha e been analysed
Le
us
conside a ypical case, Fig.
IO
shows a ypical and es ed by simula ion so wa e.
I
is necessa y he
g ey le el image and Fig.
11
i s his og am. ha dwa e implemen a ion o eal- ime, and he
A ha dwa e pla o m ha exploi s hese echniques
is
cu en ly unde de elopmen . Fo his goal. an analysis o
di e en a chi ec u es o eal ime is being made. A
dedica ed ha dwa e o w i e and ead
oi om
an
AER bus
is
being de eloped using a s anda d FPGA-based p o o yping
boa d.
I is s ill
soon
o conclude ha he Uni o m me hod
is
he
mos adequa e me hod, as
i
can be expec ed. The ha dwa e
s udy will complemen he wo k p esen ed in his pape .
1V.
CONCLUSIONS AND FUTURE WORKS
. ...
co esponding s udy.
.,
Fig.
10
Typical
image
Table
I
shows he maximum and mean no malized e o s
yields a signi ican imp o emen o e he o he me hods. I
can
be
Obse ed
ha
he
alues
o
Uni o m
Fo mo e speci ic popula ions o images, he me hods
need o be analysed o cla i y he mos app op ia e. The e
a e se e al ac o s ha can de e mine he selec ion o he
me hod o he desi ed popula ion: he necessi y
o
eal-
''
pe cen .
As
i
can
be
seen,
he
Uni o m
me hod p oposed ime, he dis ibu ion
o
e en s, whose ha e been analysed
he e.
Bu
i
can be in e es ing o analyse he e ec
o
he
me hod
sequence o e en s in a ic e ec in he AER bus, whe e
co espond o
an
image be ween he
80
and
90
o cha ge he densi y
o
in
ime
could
make
he
eSul S
o
o he
Gaussian
his og am popula ion showed
in
Fig. he me hods.
Fo
example, in biomedical applica ions, he x-
Be ween he me hods ha di ide he ime pe iod in K
ay
o
windows o slice, he Exhaus i e me hod ob ain he hes
his og am.
esul s. The Random-Squa e esul s a e be e han hose
ob ained by he Random me hod.
images
ha e
a
non-gaussian
466

V. ACKNOWLEDGMENTS
The au ho s wish o exp ess hei g a i ude o he suppo
gi en
o
his wo k by he Eu opean Commission h ough
p ojec CAVlAK ‘Con olu ion AEK Vision A chi ec u e
o Real-Time” (IST-2001-341024), unded by he
Eu opean Commission, V F amewo k P og amme
“In o ma ion Socie y Technologies
(IST)
P og amme”.
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