Towa d Visual Mic op ocesso s
TAMÁS ROSKA, FELLOW, IEEE, AND ÁNGEL RODRÍGUEZ-VÁZQUEZ, FELLOW, IEEE
In i ed Pape
This pape ou lines mo i a ions and models unde lying he
design o isual mic op ocesso s based on he cellula neu al
ne wo k uni e sal machine. We also o e iew he s a e o he a
ega ding he ealiza ion o hese mic op ocesso s in he o m
o e y la ge-scale in eg a ion chips. Examples co esponding o
measu emen s ealized on hese chips a e enclosed o illus a ion
pu poses.
Keywo ds—Analogic cellula supe compu ing, cellula neu al
ne wo ks, CNN echnology, isual mic op ocesso s.
I. INTRODUCTION
Fo mo e han 100 yea s, he li ing isual sys em o mam-
mals has been in ensi ely s udied by neu oscien is s and bio-
physicis s alike. Recen ly, compu e enginee s ha e been ac-
i e c ea ing machine ision sys ems. S ill, al hough many
ideasha ebeenp oposedandimplemen edinsilicon[1]–[3],
including esis i e g id “silicon e inas,” p og ammable cel-
lula neu al/nonlinea ne wo k (CNN)1models o he isual
pa hway, as well as many “sma op ical senso s,” no com-
ple e neu omo phic model o he opog aphic pa s o he i-
sual pa hway has been made a ailable. The eason is simple:
he lack o unde s anding o he de ailed ope a ion o many
key componen s loca ed a he on -end o he isual sys em,
no ably, he e ina and he la e al genicula e nucleus (LGN).
Hence, he ep esen a ion o he isual scene om he inpu
o he highe laye s has been unknown. O he many exci ing
Manusc ip ecei ed May 31, 2001; e ised Feb ua y 15, 2002. This
wo k was suppo ed by g an s om he Hunga ian Academy o Sciences,
he Spanish MCyT (P ojec TIC1999-0826), he Na ional Resea ch Fund
o Hunga y (OTKA), he CEE (P ojec IST-1999-19007), and he O ice
o Na al Resea ch (P ojec s N00014-00-C-0295, N68171 97-C- 9038 and
N68171 98-C-9004).
T. Roska is wi h he Analogic and Neu al Compu ing Labo a o y, MTA-
SzTaki(Hunga ianAcademyo Science)andPázmányUni e si y,Budapes
H-1111, Hunga y (e-mail: [email p o ec ed]).
Á. Rod íguez-Vázquez is wi h he Depa men o Analog and
Mixed-Signal Ci cui Design, IMSE/CNM, 41012 Se illa, Spain (e-mail:
[email p o ec ed]).
Publishe I em Iden i ie 10.1109/JPROC.2002.801453.
1Cellula neu al/nonlinea ne wo k (CNN) models we e in oduced by
Chua and Yang in 1988 [5], and hen gene alized and used as a model o
bionic eyes by Chua, Roska, and We blin [6]–[8]. Thei p inciples and ap-
plica ions o isual p ocessing a e co e ed in [9].
pa ial esul s ela ed o he isualpa hway,some ecen ind-
ings (see, o ins ance, [4]) sugges a ew sound p inciples.
• Sensing and p ocessing a e in e ac i e p ocesses, and
he p ocessing is mainly analog, combined wi h masks
o bina y (yes/no) maps.
• The basic s uc u e is composed o se e al s acks o
laye s o neu ons connec ed by local ecep i e ield o -
ganiza ions wi h di e en spa ial dis ibu ions and ime
cons an s.
• The p ocessing s a egy is a kind o “mul isc een he-
a e ”; namely, om a gi en isual scene, se e al pa -
allel maps a e gene a ed and hen u he p ocessed.
This is ue e en in he mammalian e ina [4] whe e
abou a dozen pa allel channels a e o ganized.
To implemen neu omo phic isual models on silicon, we
ha e wo ways:
• Pick up a speci ic ask and i s model and implemen i
on silicon. This is he usual way, leading o e y use ul,
ask-speci ic sma senso s.
• Make mixed-signal2 isual mic op ocesso s. Tha is,
p ocesso s which combine op ical sensing wi h analog
cellula spa ial- empo al dynamics and some o m o
logic ( hey a e called analogic p ocesso s because hey
combineanalogandlogicp ocessings uc u es),which
ha e ecep i e ields like elemen a y ins uc ions, and
he possibili y o s o ing and execu ing use -selec able
sequences o ins uc ions (p og ams).
Clea ly, he second app oach is mo e demanding in e ms o
a chi ec u e, e y la ge-scale in eg a ion (VLSI) chip design,
and compu a ional in as uc u e, leading o a new ype o
ha dwa e/so wa e sys em design.
This pape ocuses on he second app oach. Namely, we
will b ie ly e iew he analogic cellula compu e a chi ec-
u e,someCMOSp o o ypechips ela ed o ha a chi ec u e,
and he accompanying compu a ional in as uc u e. Some
examples measu ed om he so-called ACE4K chip [10] and
he CACE1K chip [11] a e included o illus a ion pu poses.
The o me has a one-laye a chi ec u e, while he la e has
a h ee-laye a chi ec u e inspi ed by he CNN model o he
2Mixed-signal means ha analog and digi al signal ep esen a ions a e
combined, and hence analog and digi al signal p ocessing.
0018-9219/02$17.00 © 2002 IEEE
1244 PROCEEDINGS OF THE IEEE, VOL. 90, NO. 7, JULY 2002
Au ho ized licensed use limi ed o: Uni e sidad de Se illa. Downloaded on Ap il 15,2020 a 14:17:43 UTC om IEEE Xplo e. Res ic ions apply.
mammalian e ina p oposed in [12] based on he disco e ies
abou he unc ionali y o he inne pa o his e ina as e-
po ed in [4].
II. CNN-BASED VISUAL MICROPROCESSORS
Back in he 1960s, he building blocks o logic design
had been he a ious logic ci cui s (mic omodules) imple-
men ing di e en “sma ” logic asks. These had also been
used o make digi al compu e s. The digi al compu e has
a key a ibu e due o J. Von Neumann, namely s o ed p o-
g ammabili y. I means ha he same co e a chi ec u e, ia
algo i hms coded in so wa e, can be used o a my iad o
asks. O , o pu i in ano he way, he a chi ec u e is open o
he human in ellec o millions o algo i hmic inno a ions.
This is he unc ional sec e behind he success o he digi al
mic op ocesso , i s made in he ea ly 70s. Visual mic op o-
cesso s aim o mimic his unc ional sec e . Howe e , hey
a emixed-signalde iceswhich ealizeanalog-and-logicspa-
ial/ empo al p ocessing asks (wa e p ocessing), and hence
equi e qui e di e en building blocks [3].
The on -end “de ices” encoun e ed in na u al ision sys-
emsa ecapableo acqui ingandp ocessingimagesinacon-
cu en manne . The e ina con ains pho o ecep o s and dy-
namicallycoupledp ocessingcellso di e en ypes.Among
many o he asks, he ea ly p ocessing ealized a he e ina
se es oex ac impo an ea u es om he awsenso yda a
and, hus, o educe he amoun o in o ma ion ansmi ed
o subsequen p ocessing. In con as o ha , image acqui-
si ion and p ocessing a e usually sepa a ed in con en ional
a i icial ision sys ems. One key aspec o isual mic op o-
cesso sis hein eg a iono sensingands o edp og ammable
p ocessing (SPP) a he analog signal a ay le el— he in e-
g a ed SPP p inciple. Among many o he hings, his allows
us o une he senso s dynamically, pixel by pixel, depending
on he con en and e en on he con ex o he changing scene.
Some o he key a chi ec u al aspec s ha e been discussed in
[13].
Some ea u es which make he isual mic op ocesso s ad-
d essed in his pape di e en om o he opog aphic sma
senso s [1], [2] include he ollowing.
• They use a co e analog p ocessing a ay (a CNN
[5]–[7]) wi h unable in e ac ion weigh pa e ns and
embedded pixel-wise da a memo ies.
• This p og ammable and econ igu able a ay is em-
bedded in a compu e a chi ec u e esul ing in he
so-called CNN uni esal machine (CNN-UM).
• The CNN-UM is s o ed p og ammable and capable
o implemen ing analogic spa ial– empo al algo i hms
h ough he sma syne gy o ha dwa e and so wa e.
All he signal a iables a e con inuous, excep o he dis-
c e eness in space (pixels o oxels). A he same ime, isual
mic op ocesso s e ain he ex ao dina y s eng h o digi al
compu e s, hei uncons ained a iabili y ia p og amming
o so wa e. Ob iously, such so wa e and ela ed algo i hms
a e di e en om con en ional ones.
Below we summa ize he main a chi ec u al and
algo i hmic ideas unde lying CNN-based isual mic op o-
cesso s. I is wo h men ioning ha al hough mos o hei
p esen -day applica ions a e ela ed o ision, many o he
Fig. 1. A ypical simple CNN s uc u e.
Fig. 2. The s anda d ou pu nonlinea i y.
opog aphic p oblems ( ac ile and audi o y), including opo-
g aphic op imiza ion, a e among he eme ging applica ions.
A. CNN Dynamics
CNNs can be ei he single-laye o mul ilaye . Conside
i s a single laye consis ing o a wo-dimensional (2-D),
egula g id o cells , whe e and a e he ow and
column coo dina es. The opog aphy o such a s uc u e is
shown in Fig. 1.
Assume each cell hos s a p ocesso wi h i s eal- alued
inpu , s a e(s), and ou pu signals, , and ,
espec i ely. In such a 2-D laye , each cell p ocesso is con-
nec ed o i s neighbo s (in a 3 3o 5 5, e c., neighbo -
hood o sphe e o in luence), deno ed by . The sim-
ples i s -o de cell s a e dynamics is gi en by3
(1)
whe e is called he h eshold o he cell
and a e called he eedback and eed-
o wa d synap ic ope a o s o empla es; in case o a 3 3
neighbo hood o adius 1, hey a e 3 3 ma ices.
The s a e and he ou pu signals o each cell a e ypically
ela ed h ough he ollowing nonlinea ou pu equa ion:
(2)
depic ed in Fig. 2. Howe e , he nonlinea i y could be o
se e al ypes and i could also be included in a simple
dynamic equa ion o m. Namely, he s anda d nonlinea i y
3The ime is scaled in he ela i e ime uni

which is he ime con-
s an o he simple i s -o de cell dyanmics.
ROSKA AND RODRÍGUEZ-VÁZQUEZ: TOWARDS VISUAL MICROPROCESSORS 1245
Au ho ized licensed use limi ed o: Uni e sidad de Se illa. Downloaded on Ap il 15,2020 a 14:17:43 UTC om IEEE Xplo e. Res ic ions apply.
Fig. 3. The ini ial pic u e and he di used pic u e using a
di usion empla e de ined by gene
G
.
in (2) and he cell-s a e dynamics ep esen ed by (1), he
so-called Chua–Yang model, could be eplaced by he
ull- ange model which means ha , and ha
he i s e m in (1) is eplaced by a nonlinea unc ion
whose shape is he in e se o ha used o he s anda d
nonlinea i y [14].
Once he cell dynamics is ixed, he in e ac ion pa e ns
and and he o se alue de ine he unc ionali y o
he CNN laye . Gi en an inpu signal a ay o
, de ined as a pic u e wi h pixel alues ,
he se o alues de e mines he ou come o he
CNN dynamic p ocess. This se is called a cloning empla e
o a gene. In he space-in a ian case, he empla es a e 3 3
(o 5 5o 7 7) ma ices. This means ha a CNN a ay
can be de ined by he cell dynamics and he 19 (o 51 o 99)
numbe s o he empla es and he o se . The inpu
image could be ei he s a ic o dynamic; hence, a CNN laye
plays he ole o an image p ocesso .
The peculia p ope y o con olling he unc ionali y o a
whole a ay o in e connec ed cells by means o jus a ew
in e connec ion weigh s (e.g., 19 numbe s) is e y amilia
o neu obiologis s. Indeed, he cloning empla e is no mo e
han a ecep i e ield o ganiza ion in he e ino opic pa o
he isual pa hway [8]. On he o he hand, he CNN pa a-
digm is well sui ed o ep esen ing many opog aphic sen-
so y modali ies ia hei ecep i e ield o ganiza ions. The
i s a emp s [15] ha e been ollowed by many o he use ul
esul s.
In a non i ial case, he CNN dynamics is a wa e ac ing
o a ini e ime . Fo example, o a di usion empla e o
gene we ha e
(3)
Fig. 3 shows he ini ial s a e and he ou pu image (a
elapsed ime). The e exis s a e y wide ca alog o empla es
co e ing a my iad o applica ions. Also, because hese em-
pla esa ep og ammablebyde ini ion,lea ning canbeinco -
po a ed o adap he empla es ei he globally, o example,
using a gene ic algo i hm [16], o locally. Thus, no only
associa i e memo ies can be cons uc ed, e.g., [17], bu he
plas ici y o he b ain migh be di ec ly modeled [13].
Fig. 4. The ex ended cell o he CNN-UM.
B. The CNN-Uni e sal Machine (CNN-UM) [7]
I we u nish each CNN cell p ocesso wi h local memo-
ies [local analog memo y (LAM) and local logic memo y
(LLM)] and a local communica ion and con ol uni (LCCU)
o send/ ecei e in o ma ion o/ om he global analogic p o-
g amminguni (GAPU), wege he ex endedCNNcell o he
CNN-UM a chi ec u e. Fo p ac ical easons, in each cell we
add a local logic uni (LLU) and a local analog ou pu uni
(LAOU) which ake inpu s and send ou pu s om/ o hei
local memo ies, LLM and LAM, espec i ely. Fig. 4 shows
he ex ended cell schema ically.
The GAPU is he conduc o o he ex ended cell a ay,
communica ing wi h each cell ia he LCCUs o each cell.
The GAPU con ains h ee egis e s and a global analogic
con ol uni (GACU), he la e o which is he hos o he
s o ed p og am and con ols he whole a ay compu e . The
h ee egis e s s o e he cloning empla es [analog p og am-
ming-ins uc ion egis e (APR)], he local logic ins uc ions
[logic p og am-ins uc ion egis e (LPR)], and he swi ch
con igu a ion codes [swi ch con igu a ion egis e (SCR)],
espec i ely.
The CNN-UM can be iewed as an a ay compu e de-
ined on lows [18]. Algo i hms can be cons uc ed whe e
he elemen a y ins uc ion is he solu ion o a pa ial di e -
en ial equa ion (PDE). This co espondence was highligh ed
al eady in he seminal pape [5] o he hea equa ion; also,
in [19], a mechanical sys em was modeled by a CNN. La e ,
sys ema ic me hods ha e been de ised o con e PDEs de-
ined in con inuous space in o CNN dynamics [20]. Recen
ad ances in compleximage p ocessing show ha PDE-based
echniques seem o be supe io in many espec s (e.g., [21]).
The d awback is hei high compu a ional complexi y when
implemen ed in digi al p ocesso s. He e, using a CNN, solu-
ion o a nonlinea PDE is he basic ask.
The nex example shows a complex analogic spa ial/ em-
po al algo i hm used o he calcula ion o he inne bound-
a ies o he le en icle in an echo-ca diog am [22]. Ac i e
wa es [23] a e used as algo i hmic s eps. Fo e e ence, we
1246 PROCEEDINGS OF THE IEEE, VOL. 90, NO. 7, JULY 2002
Au ho ized licensed use limi ed o: Uni e sidad de Se illa. Downloaded on Ap il 15,2020 a 14:17:43 UTC om IEEE Xplo e. Res ic ions apply.
Fig. 5. The bold a ows ep esen di e en cloning empla es. Some o hem a e pe o ming he
solu ion o complex nonlinea PDEs as elemen a y ins uc ions; hese a e w i en on he le -hand
side o he igu e wi h hei execu ion imes on he igh -hand side. In addi ion, se e al simple
ins uc ions and empla es a e used, o ins ance, local logic ope a ions.
also show he execu ion imes o he algo i hmic s eps on he
so-called ACE4k chip [10].
C. Example 1
A low diag am is depic ed in Fig. 5 o he analogic CNN
algo i hm wi h some ypical in e media e esul s. Obse e
ha i can be in e p e ed as a combina ion o h ee image
lows me ging and b anching du ing he p ocessing s age o
a single ame. He e he hi d low s ands o he in o ma ion
calcula ed om he cu en ame, he second one o he in-
e media e esul s ob ained om he p e ious ame, while
he i s one ep esen s he bina y masks gene a ed om he
p e ious esul . The co e o he h ee main p ocessing s ages
o he algo i hm can also be desc ibed by PDEs (le ): 1)
image il e ing and econs uc ion de i ed om nonlinea
di usion PDEs; 2) mo ion es ima ion de i ed om op ical
low PDEs; and 3) igge wa e- ype ac i e con ou -based
bounda y acking de i ed om eac ion-di usion nonlinea
PDEs. These PDE app oxima ions, execu ed on he ACE4K
chip, can be comple ed wi hin a millisecond, allowing he
p ocessing sys em o each i s peak pe o mance a ound ou
housand ame/sec ( igh ).
D. Mul ilaye and Complex Cell CNN-UM
The mul ilaye CNN s uc u e was al eady in oduced in
[5]. I is used when se e al 2-D CNN laye s a e necessa y
Fig. 6. Fig. 3 shows he ini ial s a e and he ou pu image (a
T
=2
elapsed ime). The e exis s a e y wide ca alog o empla es
co e ing a my iad o applica ions. Also, because hese empla es
a e p og ammable by de ini ion, lea ning can be inco po a ed o
adap he empla es. Ei he globally, o example, using a gene ic
algo i hm [16], o locally. Thus, no only associa i e memo ies can
be cons uc ed, e.g., [17], bu he plas ici y o he b ain migh be
di ec ly modeled [13].
o desc ibe he spa ial- empo al dynamics. In many cases,
he laye s a e jus cascaded, and he consecu i e ins uc-
ions o he CNN-UM a e adequa e o model he same
p ocess. Howe e , in hose cases whe e in e laye eedback
does exis , we need he mul ilaye CNN s uc u e. Such a
mul ilaye CNN is use ul o modeling he e eb a e e ina
[12].
Fig. 6 shows he concep ual a chi ec u e o a second-o de
dynamics, h ee-laye cell which has been p o o yped in he
ROSKA AND RODRÍGUEZ-VÁZQUEZ: TOWARDS VISUAL MICROPROCESSORS 1247
Au ho ized licensed use limi ed o: Uni e sidad de Se illa. Downloaded on Ap il 15,2020 a 14:17:43 UTC om IEEE Xplo e. Res ic ions apply.
Fig. 7. Using he CACE1K chip, p og amming he laye ime cons an s and he
A
- empla es on he
wo dynamic laye s, a double wa e p opaga ion can be p og ammed. The esul ing sequence o
snapho s shows he di e en speed and he di e en ypes o wa es on he wo laye s.
chip called CACE1K [11]. The dynamic ope a ion is gi en
acco ding o he ollowing exp essions:
(4)
whe e ep esen s he buil -in di e ence a i hme ic.
The ope a ion o his p o o ype is hence con olled by he
23 pa ame e s in ol ed in (4), gi en as
(5)
plus he ela i e alues o he ime cons an s o Laye s 1 and
2, o aling 25 di e en pa ame e s. Many ypes o nonlinea
wa es ( igge -, a eling-, au o-, and spi al-wa es) can be
ob ained by p ope ly con olling hese pa ame e s [23].
E. Example 2
This example illus a es he gene a ion o double-wa e
p opaga ion using he CACE1K chip [11]. The empla e ele-
men alues o his ope a ion a e
(6)
and he a io be ween he ime cons an s o he wo laye s is
. Using he same chip, e y ecen ly we ha e
been able o implemen some o he key inne e inal e ec s,
impossible o ealize on i s -o de laye s. Mo e de ailed e-
sul s a e epo ed elsewhe e [24].
Ou ques o make a p og ammable p o o ype spa ial- em-
po al compu e which could also se e as a isual mic op o-
cesso could be jus i ied in wo ways. On he one hand, we
ha ep o enea lie ha heCNN-UMis uni e sal.Ina sense,
i is equi alen o he Tu ing machine. The p oo was eal-
ized by implemen ing he game o li e. On he o he hand,
in each cell, wi h no mo e han ou laye s, we can imple-
men any nonlinea mul i-inpu single-ou pu ope a o wi h
ading memo y. This is only one side o he s o y. On he
o he side, which is simila o he digi al compu e s o Tu ing
machines in which he - ecu si e unc ions a e he o mal
desc ip ions o he algo i hms wi h p o en capabili ies, we
ha e also de e mined he equi alen o mal no ion o algo-
i hms as he - ecu si e unc ions wi h simila p ope ies
[18]. Hence, we ha e all he heo e ical backg ound o es-
ablish ou new ype o compu e o opog aphic ope a ions,
in pa icula o ision. Mo eo e , i has u ned ou ha he
neu omo phic cons uc s o mos o he opog aphic senses
wi h accompanying p ocessing a e qui e simila o hose o
CNN models [9].
III. ANALOGIC VISUAL MICROPROCESSOR IN SILICON
CNN-based analogic isual mic op ocesso s ha e simi-
la i ies wi h he so-called single ins uc ion mul iple da a
(SIMD) sys ems [25], al hough hey wo k di ec ly on analog
signal ep esen a ions ob ained h ough embedded op ical
senso s and hence do need nei he a on -end senso y plane
no analog- o-digi al con e e s. The a chi ec u e o hese
isual mic op ocesso s is illus a ed in Fig. 8 h ough wo
p o o ype chips, namely, ACE4K [10] and ACE16K [26].
In bo h cases, as in o he ela ed chips [11], [27]–[29], he
a chi ec u e includes a co e a ay o in e connec ed elemen-
a y p ocessing uni s, su ounded by a global ci cui y. This
la e ci cui y is in ended o :
• con ol and iming;
• ad essing and bu e ing o he co e cells;
• inpu /ou pu ;
• s o age o use -selec able ins uc ions (p og ams) o
con ol he sequence o ope a ions o he p ocessing
co e;
• s o age o use -selec able analogic p og amming pa-
ame e con igu a ions ( empla es).
1248 PROCEEDINGS OF THE IEEE, VOL. 90, NO. 7, JULY 2002
Au ho ized licensed use limi ed o: Uni e sidad de Se illa. Downloaded on Ap il 15,2020 a 14:17:43 UTC om IEEE Xplo e. Res ic ions apply.

Fig. 8. A chi ec u es o analogic isual mic op ocesso chips: (a) ACE4K [10] and (b) ACE16K
[26].
On he o he hand, he co e o in e connec ed p ocessing
uni s embeds di e en unc ions on a common silicon sub-
s a e (see Fig. 9 o illus a ion pu poses), namely:
• 2-D sensing;
• 2-Danalog/digi ala ayp ocessingconcu en wi h he
signal sensing;
• 2-D spa io- empo al p ocessing de e mined by local,
ecep i e- ield-like p og ammable in e connec ions;
• 2-D memo y banks o concu en online uploading
and downloading o sho - e m analog and digi al
da a.
Se e al analogic isual mic op ocesso chips in di e en
CMOS echnologies ha e been epo ed du ing he las ew
yea s. Pa icula ly, [10], [11], and [26]–[29] epo hose im-
plemen a ions wi h a leas 20 20 pixels. Table 1 p esen s a
summa y o some o hei mos ele an da a. Some columns
ROSKA AND RODRÍGUEZ-VÁZQUEZ: TOWARDS VISUAL MICROPROCESSORS 1249
Au ho ized licensed use limi ed o: Uni e sidad de Se illa. Downloaded on Ap il 15,2020 a 14:17:43 UTC om IEEE Xplo e. Res ic ions apply.
Fig. 9. llus a ing he embedding o di e en unc ional ea u es a he co e p ocessing a ay
o isual mic op ocesso s. (a) Mic opho og aph o he ACE4K chip (le ) and concep ual
ep esen a ion o he dis ibu ed unc ions embedded in he co e a ay ( igh ). (b) Layou o a
p ocessing uni o he ACE16K showing he a eas occupied by he di e en unc ions ealized
concu en ly by he co e a ay.
co espond o chips in ended o black and whi e inpu im-
ages,while o he sa e o chips whichaccep g ay-scaleinpu
images. As wi h any o he analog p ocessing ci cui , igu es
o me i abou pe o mance mus con empla e accu acy and
a ea occupa ion in addi ion o speed and powe consump-
ion. The speed measu e he e is p opo ional o he numbe
o cells, he in e se o he ime cons an , and a weigh ed
numbe o mul iplie s pe cell. Any compa ison mus e e
o he numbe o ope a ions pe second and o he accu acy.
The da a in he able highligh s he ollowing.
• The e is a adeo be ween a ea occupa ion (cell den-
si y)andaccu acy,on heonehand,andspeedo ope a-
ionandpowe consump ion,on he o he . This adeo
is ypical o analog in eg a ed ci cui s [33].
• Thee olu ion owa dscaled-down echnologies epo s
ad an ages in e ms o speed and cell densi y. Ac u-
ally, he ACE16K chip has 128 128 esolu ion and
is capable o ealizing sequences o 64 ins uc ions;
using up o 32 di e en empla es (each empla e con-
sis ing o 24 8-bi -coded analog p og amming alues)
du ing a sequence; loading and downloading ull-size
g ay-scale images o and om he cache memo y, and
ha ingalwayseigh ull-sizeimagesa ailable o usage
du ing he low; wi h an in e nal p ocessing ime o
160 ns, and p o iding digi ally coded ou pu images
(ob ained wi h a ba e y o in e nal con e e s) wi h a
downloading ime o 0.128 ms.
The capabili y o design cells wi h maximum densi y,
speed and accu acy, and minimum a ea and powe consump-
ion elies basically on he exploi a ion o all unc ional
ea u es o e ed by he MOS ansis o . This is e y di e en
om digi al design, in which only he swi ching capabili y
o he MOS ansis o is exploi ed. The design o he en i ies
which in e connec he cells (synapses) de ines one o he
1250 PROCEEDINGS OF THE IEEE, VOL. 90, NO. 7, JULY 2002
Au ho ized licensed use limi ed o: Uni e sidad de Se illa. Downloaded on Ap il 15,2020 a 14:17:43 UTC om IEEE Xplo e. Res ic ions apply.
Table 1
Summa y and Compa ison o Chip Implemen a ions
majo issues. In o de o do his, di e en possibili ies may
be chosen a p io i, as illus a ed in Fig. 10. In all cases,
elec ical con ollabili y is p o ided by de aul . Howe e , he
di e en s a egies exhibi qui e a di e en pe o mance in
he p esence o sys ema ic and andom e o sou ces, as well
as a di e en incidence o he global signal ansmission
e o s. Hence, ca e ul analysis and op imiza ion is needed
o selec he bes app oach. Such analysis and op imiza ion
a e needed o achie e he cell densi y and accu acy le els
ea u ed by las gene a ion chips. The backg ound o such
p ocedu es can be ound in [3], [10], [11], [26], and [28].
IV. ABOUT SCALING DOWN
I is expec ed ha he pe o mance igu es ea u ed o
hese chips can be u he enhanced as echnology scales
ROSKA AND RODRÍGUEZ-VÁZQUEZ: TOWARDS VISUAL MICROPROCESSORS 1251
Au ho ized licensed use limi ed o: Uni e sidad de Se illa. Downloaded on Ap il 15,2020 a 14:17:43 UTC om IEEE Xplo e. Res ic ions apply.
Fig. 10. Using a single NMOST o ol age- o-cu en ans o ma ion. Only i s -o de e ms a e
included in he displayed beha io al equa ions.
down. Howe e , one p oblem a ises due o he necessi y o
main aining analog accu acy, and hence he quali y o he
analog design, as ansis o sizes dec ease. Below we i s
iden i y misma ch as he main limi o he analog accu-
acy and hen explo e di e en adeo s associa ed wi h he
analog design in he p esence o misma ch.
A. Misma ch Ve sus Noise as a Limi ing Fac o
Misma ch makes wo nominally iden ical de ices beha e
di e en ly when hey a e used in a eal in eg a ed ci cui .
Basedon he o mula iono misma chas a unc iono de ice
geome ies in [30], he a iance o he la ge-signal anscon-
duc ance pa ame e , he h eshold ol age , and he
slope ac o 4as unc ion o he de ice a ea and aspec
a io can be ep esen ed as
(7)
whe e is he ansis o channel a ea and is he ansis o
aspec a io.
Ano he accu acy limi ing ac o is noise. The equi alen
noise cu en o an MOS ansis o can be exp essed as [31]
(8)
whe e and a y be ween 1 and 2,
wi hin he ohmic egion and o his quan-
i y in sa u a ion, and is he small-signal
ansconduc ance pa ame e .
4In he o iginal model, he a iance was o mula ed o he body e ec
ac o

1

(
n
)
can be ob ained as a unc ion o

(
V
)
and

(

)
.
Le us conside ha he only signi ican misma ch e o
is ha o he la ge-signal ansconduc ance pa ame e —as
i ac ually happens in many p ac ical ci cui s used o es-
ablishing in e connec ions in analog a ay p ocesso s [32],
[33]. In e ms o he ansis o a ea and aspec , his e o
is exp essed as
(9)
Unde simila assump ions, he noise con ibu ion can be ap-
p oxima ed by
(10)
Using ypical pa ame e s o CMOS 0.5- m echnologies
(V, V, V,
cm V s , m ,
V F) and conside ing a bandwid h o 1–5 MHz, we conclude
ha , o de ices wi h channel a eas o abou 50 m , he
ma ching le el se s an accu acy sligh ly abo e 8 b while o
his same a ea and a channel aspec a io o 0.1 he noise
poses a limi in he esolu ion o 10.48 bi , a beyond om
ha posed by misma ching phenomena.
B. The E ec o he Scaling P ocess
Le us assume ha la e al dimensions scale as
(11)
Thus, he ga e oxide hickness, which app oxima ely e ol es
in cu en echnologies as , scales as
(12)
1252 PROCEEDINGS OF THE IEEE, VOL. 90, NO. 7, JULY 2002
Au ho ized licensed use limi ed o: Uni e sidad de Se illa. Downloaded on Ap il 15,2020 a 14:17:43 UTC om IEEE Xplo e. Res ic ions apply.