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SynFull-RTL: E alua ion Me hodology
o RTL NoC Designs
Neiel Ley a, Ba celona Supe compu ing Cen e , Uni e si a Poli `
ecnica de Ca alunya,
Ali eza Monemi, Ba celona Supe compu ing Cen e , and En ique Vallejo, Uni e sidad de Can ab ia, ICS FORTH
Abs ac —SynFull is a widely employed ool ha gene a es
ealis ic a ic pa e ns o he pe o mance e alua ion o a
NoC. In his wo k, we iden i y he main limi a ions o SynFull:
high a iabili y and long simula ion ime and also ha hese
limi a ions inc ease when SynFull is in eg a ed wi h RTL designs.
SynFull-RTL employs a s a is ical app oach, simula ing each
applica ion mac o-phase only once and a e aging acco ding o i s
p obabili y o occu ence and he measu ed a ic load. SynFull-
RTL ob ains highe accu acy han he o iginal e sion and
educed a iabili y, wi h obse ed 40× educ ion in simula ion
ime and esou ces. A use-case wi h P oSMART alida es he
esul s.
Index Te ms—SynFull, Ve ila o .
I. INTRODUCTION
SIMULATION is e y impo an s ep o e alua e he pe o -
mance o new designs. The ideli y o he esul s depends
mainly on he cha ac e is ics o he ne wo k and he a ic
model used. The mo e de ailed a ic in he model is used,
he highe he accu acy o he esul s, bu also he highe he
load and he simula ion ime.
SynFull [1] in oduces a Ma ko -chain-based syn he ic
model ha mimics eal applica ion a ic. I is e y a ac i e,
because i allows o eed he simula ion wi h a ealis ic a ic
pa e n in a simple and as way. SynFull di ides he simula ion
in o di e en long pe iods o ime, deno ed mac o-phases,
de i ed om aces o a eal execu ion o he applica ion.
Howe e , in his pape we iden i y ha SynFull does no
execu e all he phases in he p opo ion ound in he eal
applica ion. Fo his eason, he a iabili y o he gene a ed
a ic may be e y la ge and he esul s o he simula ions
become un eliable, since di e en execu ions using he same
SynFull model may ecei e a ic ha signi ican ly di e s.
This may be emedied by unning e y long simula ions, bu
i inc eases compu a ional equi emen s.
In his wo k, we modi y SynFull o eed an RTL simula-
o . Because o i s inc eased simula ion equi emen s, u he
enleng hening simula ions o mi iga e he inna e a iabili y
becomes un easible. To a oid hese p oblems, we in oduce
a no el me hodology based on SynFull, deno ed SynFull-
RTL, which is bo h as e and mo e p ecise han he o igi-
nal implemen a ion. Al hough he p oposed me hodology is
no exclusi e o RTL simula ion, i is pa icula ly ele an
Manusc ip ecei ed XXX, 2022; e ised XXX, 2022; accep ed XXX,
2022. This a icle was p esen ed a he 2022 In e na ional Symposium on
Ne wo ks-on-Chip and appea s as pa o he Design & Tes special issue.
o hese models because o hei inc eased simula ion cos .
SynFull-RTL e alua es each mac o-phase in isola ion, and de-
e mines he a e age la ency esul acco ding o he heo e ical
p obabili y o occu ence o each mac o-phase in s eady-s a e
and i s measu ed a ic le el.
The me hodology in oduced in his wo k p o ides measu e-
men s as e and wi h high accu acy, le e aging he SynFull
models based on eal applica ions. The main con ibu ions a e:
•An in eg a ed model o SynFull wi h RTL NoC ou e
models, which allows o quickly e alua e he pe o mance
o he model using ealis ic a ic.
•An analysis o he main limi a ions o SynFull in his
en i onmen , mainly he use o a single seed, i s high
a iabili y and he long simula ion imes.
•SynFull-RTL, an e alua ion me hodology ha samples
SynFull mac o-phases and a e ages hem acco ding o
hei p obabili y o occu ence and hei a ic le el.
•An e alua ion o SynFull-RTL, which shows ha i can
p o ide mo e accu a e esul s han he o iginal SynFull
app oach while equi ing 8× o 40×less simula ed cycles
and educing ime- o-solu ion by up o 40×.
II. BACKGROUND
A. T a ic Modelling
T a ic modelling is e y ele an o accu a e pe o mance
e alua ion o NoCs. The e a e mul iple me hodologies o
gene a e a ic, including syn he ic models (such as andom
uni o m o pe mu a ions), use o la ge aces such as Ne-
ace [2] o simula ing a whole applica ion, such as gem5
modelling. T aces o whole applica ion simula ion p o ide he
highes accu acy, bu hey equi e huge iles, la ge amoun s o
memo y and long simula ion ime. [2] p esen s an in e es ing
discussion and o e iew o he limi a ions o di e en models.
Fo he designe s, a ele an ea u e is execu ion ime; ha
is, he designe s o en need o es a new expe imen al ea u e
in he NoC and ha e a hin abou i s es ima ed pe o mance. In
hese cases, o wai ing imes is undesi able, because i delays
decisions abou new ea u es and possible changes.
B. SynFull
SynFull is a ool designed o acili a e as and ealis ic
NoC e alua ions. SynFull gene a es andom a ic based on
eal applica ions, wi hou elying on aces o ull-sys em
simula ion. SynFull has been used in many ele an esea ch
wo ks, such as [3]–[7].
0000–0000/00$00.00 © 2021 IEEE
© 2022 IEEE. Pe sonal use o his ma e ial is pe mi ed. Pe mission om IEEE mus be ob ained o all o he uses, in any cu en o u u e media, including ep in ing/
epublishing his ma e ial o ad e ising o p omo ional pu poses,c ea ing new collec i e wo ks, o esale o edis ibu ion o se e s o lis s, o euse o any copy igh ed
componen o his wo k in o he wo ks. DOI 10.1109/MDAT.2022.3202996
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Mphase_1
P1=45.78%
100%
96.53% Mphase_2
P2=1.88%
0.86%
Mphase_3
P3=52.32%
2.59%
15.78%
78.94%
5.26%
2.46%
0%
97.53%
Fig. 1. S a e diag am o he mac o-phases in he Ba nes model. The numbe
nex o each a ow indica es he ansi ion p obabili y.
SynFull di ides each applica ion in o blocks called mac o-
phases, each o hem las ing o an in e al o 500,000 cycles.
Each mac o-phase models he beha io o an in e al o he
applica ion. Each mac o-phase has di e en cha ac e is ics,
such as he packe injec ion a e and he pa e n o des ina ions.
SynFull models de ine mac o-phases as nodes o a Ma ko
chain. Each model de ines a low numbe (2 o 10 in he
a ailable models) o mac o-phase ypes, ob ained om aces
o he ac ual applica ion. Inside each mac o-phase, he model
ha de ines he a ic is deno ed mic o-phase, and i also
ollows an in e nal Ma ko model. The model also de ines
he ansi ion p obabili y be ween mac o-phases iand j,pij,
and he p obabili y o occu ence o each mac o-phase iin
s eady-s a e, Pi. In a Ma ko model, hey a e ela ed by
Pj=Pi(Pi×pij).
Figu e 1 depic s he mac o-phase s uc u e o he Ba nes
model. I comp ises 3 mac o-phase ypes, each one wi h a
di e en p obabili y o occu ence (P1,P2, and P3). No e
ha hese p obabili ies di e la gely, wi h P2being e y
low. T ansi ion p obabili ies (a ows) indica e how likely is
o change om one mac o-phase ype o ano he , a e each
in e al. The s a ing mac o-phase is always ype 1.
A simula ion wi h SynFull injec s a ic acco ding o he
cu en mac o-phase ype o he comple e in e al o 500,000
cycles. Then, i andomly changes o ano he mac o-phase,
acco ding o he ansi ion p obabili ies in he model. Al hough
SynFull implemen s a ha dcoded andom seed, he sequence o
mac o-phases simula ed is no always he same o each a ic
model. This issue, which is u he analyzed in Sec ion III-B,
occu s because he andom sequence a ies wi h packe ecep-
ion, which in u n depends on he simula ed NoC model.
C. P oSMART
P oSMART1[8] is a ully pa ame izable NoC ou e de-
sign w i en in Sys emVe ilog, in eg a ed wi h a module ha
suppo s mul ihop bypass. The NoC is con igu able wi h many
s a e-o - he-a ea u es such as i ual channels, i ual ne -
wo ks, ha d-buil -in QoS, mul icas , mul ihop bypass, di e en
ou ing algo i hms, and ne wo k ypologies.
P oSMART allows injec ed li s o skip mul iple ou e s
in a dimension wi hin a single cycle, esul ing in a d as ic
1h ps://gi hub.com/amonemi/P oNoC
la ency educ ion. The sys em employs he HPCMax pa-
ame e o de ine he maximum mul ihop leng h; in a 4×4
mesh, HPCMax a ies om 1 o 3. This allows o model
bo h a adi ional mesh and one wi h mul ihop bypass. The
e ec i eness o P oSMART is only e alua ed in [8] using
syn he ic a ic pa e ns, and i lacks empi ical e alua ions
using ealis ic a ic pa e ns. This wo k employs P oSMART
as a use-case o alida e he p oposed e alua ion me hodology.
III. SYNFULL-RTL INTEGRATION AND ANALYSIS OF
LIMITATIONS
This sec ion in oduces he in eg a ion o SynFull wi h
an RTL NoC model o suppo ealis ic RTL simula ions. I
ollows wi h an analysis o he main limi a ions o SynFull.
A. In eg a ion o P oSMART wi h SynFull
The P oSMART GUI p o ides a se o au oma ion oolse
ha gene a es a simula ion model o any cus om-de ined
NoC model and also in eg a es se e al ools o acili a e
pe o mance e alua ion. This sec ion discusses wo ools used
o in eg a ion wi h SynFull: Ques asim, and Ve ila o .
1) Ques asim: I wo ks di ec ly on RTL code and uses he
DPI unc ions o wo k wi h o eign p og amming languages
like C++. The C++ module is compiled sepa a ely as a sha ed
bina y and he loca ion is passed o Ques asim wi h a lag, so
i can ind he bina y a simula ion ime. This ool was used
in he i s app oach o SynFull and i was decided o keep
he socke communica ion. The DPI unc ion has wo pa s,
one is so wa e and ano he one is RTL code. In so wa e,
ecei ing packe s om he socke is handled and he packe s
a e mapped on a di ec connec ion o he RTL pa o he DPI
unc ion, whe e we ha e po s o all he inpu queues whe e
each packe is di ided in o li s.
2) Ve ila o : I con e s he NoC RTL code o an equi alen
cycle-accu a e C++ model using Ve ila o simula o . Then i
alloca es h ee di e en so wa e-based queues o each NoC’s
endpoin namely as injec ion, a e sal, and ejec ion queues.
These queues keep he desi ed a ic packe s’ in o ma ion
such as injec ion imes amp, he des ina ion node add ess,
and packe size in li s. The simula ion model moni o s he
injec ion queues and eeds a co esponding packe o he NoC
acco ding o injec ion imes amp and NoC c edi a ailabili y.
Injec ed packe s a e mo ed o a e sal queues and emain
he e un il hei co esponding packe s each hei des ina ion.
A ejec ion ime, packe s a e ans e ed o ejec ion queues.
The simula ion model collec s se e al s a is ical in o ma ion
du ing he simula ion and epo s he pe o mance esul s a
he end o he simula ion.
The P oSMART baseline simula ion model can be e o -
lessly adop ed wi h any exis ing a ic gene a o model such as
Ne ace o SynFull lib a ies. The in eg a ion is s aigh o wa d,
and i only equi es he mapping o injec ion/ejec ion unc ions
om he a ic gene a o lib a y o he P oSMART so wa e-
based injec ion/ejec ion queues.
Fo he ollowing analysis and esul s, he e sion wi h
Ve ila o is used, because i allows he execu ion o mul iple
simula ions in pa allel wi hou license es ic ions.
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HPC_Max=1 HPC_Max=2 HPC_Max=3
HPC_Max=1 HPC_Max=2 HPC_Max=3
Maximum Mul ihop Leng h
0
2
4
6
8
10
12
14
16
18
20
A g. Fli La ency (Cycles)
(a) A e age Fli La ency
HPC_Max=1 HPC_Max=2 HPC_Max=3
Maximum Mul ihop Leng h
0
100000
200000
300000
400000
500000
600000
Numbe o Packe s Injec ed
(b) Packe Injec ion
Fig. 2. Va iabili y obse ed in he simula ion esul s o 10 million cycles (20
mac o-phase i e a ions) using he o iginal SynFull unning he Ba nes model.
B. Analysis o SynFull Limi a ions
This subsec ion p esen s some limi a ions and challenges
ha we e encoun e ed du ing he in eg a ion o SynFull wi h
P oSMART. Fi s , i highligh s ha , e en wi h a single ha d-
coded seed, mino di e ences in he NoC model cause la ge
di e ences in obse ed pe o mance. Nex , i analyzes he a i-
abili y, con i ms ha i comes om di e ences in he mac o-
phases execu ed, and obse es ha gene a ed a ic may no be
comple ely ep esen a i e o he o iginal applica ion. Finally,
i discusses simula ion ime.
1) Single seed and la ge a iabili y: SynFull makes use o
a pseudo andom numbe gene a o , which elies on a ha d-
coded seed alue. Ha ing a ixed seed always gene a es he
same sequence o alues o consecu i e andom gene a ions.
Howe e , we obse e ha in SynFull his does no gua an ee
he same sequence o mac o-phases and, hus, an equal o e y
simila a ic pa e n o each model.
An analysis o he implemen a ion o SynFull e eals ha
andom calls a e employed o mac o- and mic o-phase an-
si ions as well as packe gene a ion. Packe gene a ion occu s
bo h a he s a o each mac o-phase and in esponse o packe
ecep ion. The use o di e en NoC models modi ies he delay
o each packe , and in u n he e olu ion o he pseudo andom
sequence and he ansi ion be ween mac o-phases.
Figu e 2 illus a es he impac o his issue wi h h ee
execu ions o Ba nes wi h di e en alues o HP CMax in
P oSMART. I shows packe injec ion and a e age li la ency.
La ge HP CMax alues allow o highe mul ihop leng h and
a e expec ed o educe la ency, which may inc ease a ic.
Howe e , esul s in Figu e 2b show ha load is much highe
wi h HPCMax = 2 han HPCMax = 1 o HPCMax = 3.
Simila ly, Figu e 2a shows ha appa en ly HPCMax = 2
ob ains he bes li la ency. The compa ison is e oneous,
because he a ic injec ed in each case di e s signi ican ly.
In conclusion, he a ic p esen s a la ge a iabili y in bo h
he a ic load and a e age la ency be ween di e en simu-
la ions, and he use o a ixed seed p e en s om obse ing
di e en ou comes, esul ing in e oneous conclusions.
2) Va iabili y and applica ion ep esen a i eness: We ex-
plo e a modi ied e sion o SynFull ha accep s a seed alue
as a pa ame e . Figu e 3 shows he amoun o a ic in se e al
simula ions o FFT and Ba nes using 10 di e en seeds. Each
simula ion uns o 10 million cycles (20 mac o-phases o
43233165
12132064
36361438
91167016
96285072
97865544
13917556
18219912
79952726
49250622
Seeds
0
2000000
4000000
6000000
8000000
Numbe o Packe s
Injec ed
HPC_Max=1 HPC_Max=2 HPC_Max=3
(a) FFT model
43233165
12132064
36361438
91167016
96285072
97865544
13917556
18219912
79952726
49250622
Seeds
0
400000
800000
1200000
1600000
Numbe o Packe s
Injec ed
HPC_Max=1
HPC_Max=2
HPC_Max=3
(b) Ba nes model
Fig. 3. Numbe o packe s injec ed in 10 million cycles using he o iginal
SynFull and wo di e en applica ion models, o di e en seed alues.
500,000 cycles). The la ge a iabili y obse ed wi hin a single
seed also occu s o di e en seed alues.
SynFull models comp ise di e en mac o-phases, each one
wi h di e en mic o-phase pa e ns o a ic injec ion and
load. The numbe o mac o-phases depends on he applica ion
model; FFT and Ba nes con ain 5 and 3 di e en mac o-phases
espec i ely. Figu e 4 dissec s he amoun o a ic injec ed in
each mac o-phase in bo h applica ions. Wi hin a mac o-phase
he amoun o a ic is almos cons an , and ba ely depends
on he mul ihop dis ance used in he ou e model. E o ba s
in hese igu es, which ep esen one s anda d de ia ion, a e
ba ely isible. Simila ly, he a e age la ency pe mac o-phase
p esen ed in Figu e 5 is also qui e cons an .
These analyses con i m ha he a iabili y in he applica ion
a ic load obse ed in Figu es 2 and 3 does no come om
mic o-phase a iabili y, bu i depends on he speci ic mac o-
phases simula ed in each case. An analysis o he simula ions
con i ms ha he execu ions in Figu e 3 wi h a highe injec ed
load also simula e a highe numbe o mac o-phases wi h high
load (phase 5 in FFT and phase 2 in Ba nes), and ice e sa.
Figu e 6 shows a e age li la ency esul s o h ee appli-
ca ions unning o 10 million cycles, a e aging esul s om
10 di e en seeds. The s anda d de ia ion is signi ican , so he
esul is no eliable. The high s anda d de ia ion is caused by
he high a iabili y o SynFull’s packe injec ion p ocess.
To gua an ee ha a simula ion co esponds o he s eady
s a e si ua ion, his is, i p ope ly ep esen s he applica ion
om which he model was cap u ed, he obse ed pe cen age
o occu ence o each mac o-phase should be simila o he
s eady-s a e p obabili y o occu ence o such mac o-phase.
Howe e , his clea ly does no occu in he di e en execu ions
shown in Figu e 3. Indeed, in some cases we obse ed ha
a gi en mac o-phase (wi h low p obabili y o occu ence) is
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Mphase_1 Mphase_2 Mphase_3 Mphase_4 Mphase_5
Mac o-phases
0.0
0.2
0.4
0.6
0.8
1.0
1.2
A g. Packe s Injec ed
1e7
HPC_Max=1
HPC_Max=2
HPC_Max=3
(a) FFT
Mphase_1 Mphase_2 Mphase_3
Mac o-phases
0
400000
800000
1200000
1600000
2000000
2400000
2800000
3200000
A g. Packe s Injec ed
HPC_Max=1
HPC_Max=2
HPC_Max=3
(b) Ba nes
Fig. 4. Pe mac o-phase numbe o injec ed packe s, o 10 million cycles
(20 mac o-phase i e a ions), a e aging esul s om 10 di e en seeds.
Mphase_1 Mphase_2 Mphase_3 Mphase_4 Mphase_5
Mac o-phases
8
10
12
14
16
18
20
22
A g. Fli La ency (Cycles)
HPC_Max=1
HPC_Max=2
HPC_Max=3
(a) FFT
Mphase_1 Mphase_2 Mphase_3
Mac o-phases
10
12
14
16
18
20
A g. Fli La ency (Cycles)
HPC_Max=1
HPC_Max=2
HPC_Max=3
(b) Ba nes
Fig. 5. Pe mac o-phase a e age li la ency, unning o 10 million cycles
(20 mac o-phase i e a ions), a e aging esul s om 10 di e en seeds.
ne e execu ed in ce ain simula ions.
Longe simula ions wi h a highe numbe o a e aged
simula ions may be used o inc ease he a ic ideli y. Figu e 7
shows he a e age numbe o injec ed packe s o FFT and
Ba nes as he numbe o cycles inc eases, a e aging he esul
o 10 di e en seeds. As expec ed, he a e age numbe o
packe s g ows p opo ionally o he simula ion leng h. How-
e e , he obse ed s anda d de ia ion emains e y la ge, e en
a e aging simula ions o 400 million cycles.
Se e al conclusions can be ob ained om he p e ious anal-
ba nes body ack
Applica ion Models
11
13
15
17
19
21
A g. Fli La ency (Cycles)
HPC_Max=1 HPC_Max=2 HPC_Max=3
Fig. 6. La ency Resul s o 10 million o cycles in P oSMART+SynFull,
a e aging 10 di e en seeds.
HPC_Max=1 HPC_Max=2 HPC_Max=3
Maximum Mul ihop Leng h
0
1
2
3
4
5
6
7
8
A g. Packe s Injec ed
1e7
5M
10M
20M
50M
100M
200M
400M
(a) FFT
HPC_Max=1 HPC_Max=2 HPC_Max=3
Maximum Mul ihop Leng h
0.0
0.2
0.4
0.6
0.8
1.0
1.2
A g. Packe s Injec ed
1e7
5M
10M
20M
50M
100M
200M
400M
(b) Ba nes
Fig. 7. Numbe o injec ed packe s o di e en leng hs o a SynFull
simula ion, om 5 o 400 million cycles, and esul ing a iabili y.
ysis. Fi s , a single simula ion may no ep esen accu a ely he
beha iou o he applica ion om which he SynFull model
was ob ained, because he mac o-phases a e no execu ed p o-
po ionally o hei expec ed p obabili y o occu ence. Indeed,
some mac o-phases may no be execu ed a all. Second, in
o de o accu a ely model he applica ion, SynFull equi es
a e aging mul iple simula ions wi h long simula ion imes.
3) Simula ion ime: RTL designs a e mo e complex and
de ailed han he unc ional models in so wa e simula o s such
as BookSim. Fo his eason, RTL simula ions consume mo e
esou ces and a e slowe han high-le el so wa e simula ions.
The p e ious analysis sugges s ha a la ge numbe o
simula ed cycles a e equi ed o con e gence, such as 400
million. Running in a ypical HPC clus e , we measu ed an
a e age 5.4 hou s o simula ion using SynFull wi h Booksim.
Howe e , using SynFull in eg a ed wi h an RTL design like
P oSMART, we measu ed a e age simula ion imes o 39
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hou s, 7.2×slowe . Besides he inc eased amoun o com-
pu a ion esou ces, due o hei slow execu ion speed, RTL
simula ions may exceed he maximum allowed job un ime
in some o all he clus e queues, o cing he use o longe
queues wi h lowe p io i y and ypically mo e conges ion. All
hese e ec s educe he bene i o SynFull as a mechanism o
as e alua ion o RTL NoC designs wi h ealis ic a ic.
IV. SYNFULL-RTL METHODOLOGY
This sec ion in oduces SynFull-RTL, which sol es he
limi a ions o SynFull in RTL models iden i ied in Sec ion III.
SynFull-RTL employs he o iginal SynFull models, bu exe-
cu es each mac o-phase in isola ion and a e ages he esul s
acco ding o he s eady-s a e p obabili ies in he model.
Ou p oposal simula es each mac o-phase indi idually,
a oiding a la ge a ia ion in packe injec ion in each simu-
la ion. Figu es 4 and 5 show ha he s anda d de ia ion o
a e age packe injec ion and a e age li la ency pe mac o-
phase can be educed o negligible alues. SynFull-RTL
employs wo pa ame e s: Fi s , N ep esen s he numbe o
di e en simula ions (di e en seeds) ha a e a e aged pe
mac o-phase. N= 10 is used in Figu es 4 o 7. Second, L
ep esen s he leng h o each simula ed mac o-phase in each
simula ion, in i e a ions. Since each i e a ion co esponds o
a ixed alue o 500,000 cycles, Lcan be also indica ed
in cycles. Finally, he numbe o dis inc mac o-phases M
depends on he applica ion model. The M×Nsimula ions
can un in pa allel, educing he ime- o-solu ion.
Fo each simula ion i(1 ≤i≤N)co esponding o
mac o-phase m(1 ≤m≤M)we ob ain he a e age li
la ency Fli La im, he a e age packe la ency Pk La im,
and he numbe o sen packe s NumPk im and sen li s
NumFli sim. The applica ion model p o ides he p obabili y
o each mac o-phase, Pm. F om hese alues, we de i e he
a e age and he s anda d de ia ion o he packe and li
la ency in s eady s a e. The ollowing discussion explains how
o calcula e packe la ency, and he calcula ion o li la ency
is analogous.
Fo each mac o-phase m, he a e age and he
s anda d de ia ion o he packe la ency (A gPk La m
and Sde Pk La m) and a e age numbe o packe s
(A gNumPk m) a e calcula ed om he esul s o he N
simula ions. To ob ain he o e all esul s, we canno simply
a e age he esul s om all he Mmac o-phases, because hey
di e in hei p obabili y Pmand hei load in ensi y. Ins ead,
he alue mus be calcula ed using a weigh ed a e age, in
which each weigh wm ep esen s he o e all pe cen age o
packe s ha a e injec ed in mac o-phases o ype m.
A no malized o e all numbe o packe s is de ined by
PM
m=1(A gNumPk m×P(m)), which conside s he con-
ibu ion o all he mac o-phase ypes. The e o e, he weigh
o be used o each mac o-phase is de ined in Equa ion 1:
wm=A gNumPk m×P(m)
PM
m=1(A gNumPk m×P(m)) (1)
Wi h hese weigh s ha ep esen he con ibu ion o each
mac o-phase ype, he a e age packe la ency can be simply
calcula ed as:
A gPk La =
M
X
m=1
(wm×A gPk La m)(2)
To es ima e he accu acy o he mechanism, we also cal-
cula e he s anda d de ia ion o he la ency es ima ion. The
s anda d de ia ion is he oo o he a iance. The a iance o
a linea combina ion o andom a iables is gi en by:
V a (
M
X
i=1
ai·Xi) =
N
X
i=1
a2
i·V a (Xi)
+ 2 X
1≤i<j≤N
ai·aj·Co (Xi, Xj)
(3)
Fo independen execu ions he co a iance Co (Xi, Xj)is
null, so we can es ima e:
V a Pk La =
M
X
m=1
w2
m×Sde Pk La 2
m(4)
And inally we ob ain he s anda d de ia ion as
Sde Pk La =√V a Pk La .
V. EVALUATION
This sec ion p esen s an e alua ion o he accu acy and
alidi y o he SynFull-RTL model, ollowed by a use-case
e alua ing he la ency imp o emen s o he P oSMART ou e .
Me hodology: The modelled ne wo k is a 4×4mesh using
DOR and 1 i ual channel, wi h he P oSMART ou e [8]
wi h HPCMax = 1,2,3in all cases. Unless o he wise
no ed, he e alua ions in his sec ion employ SynFull-RTL wi h
L= 20 i e a ions pe mac o-phase and N= 5 seeds. In o de
o quan i y he inaccu acy o ou s a is ical me hodology2, we
also conside a SynFull-RTL-Ideal model, which conside s
L= 400 i e a ions and N= 20 seeds. Whi hese la ge
pa ame e s, he cycle coun is so la ge ha a a ia ion in he
numbe o seeds does no modi y he esul s in a no iceable
way. E alua ions using he o iginal SynFull a e age N= 10
di e en seeds ( o ob ain simila con idence in e al), unning
o he speci ied ime.
Plo s include he con idence in e al o a 95% con idence
le el. This in e al is p opo ional o he s anda d de ia ion
depic ed in p e ious sec ions. In pa icula , i is calcula ed as
CI =±Z·s
√N, wi h Z= 1.960 o a 95% con idence le el,
s he s anda d de ia ion and N he numbe o samples.
SynFull-RTL pa ame e s: Figu e 8 analyzes he impac o
he numbe o a e aged seeds Nand he numbe o i e a ions
Lo each mac o-phase in SynFull-RTL. The applica ion em-
ployed is Ba nes, al hough o he applica ions beha e simila ly.
The a e age la ency in Figu e 8a ha dly depends on he
numbe o a e aged simula ions, N. Using N≥5p o ides an
e o in e al wi hin a 1%. In Figu e 8b, N= 5 simula ions
wi h di e en seeds a e a e aged, bu he numbe o in e als
simula ed pe mac o-phase ange om L= 1 o L= 20.
The ini ializa ion impac , his is, he e o obse ed when he
numbe o i e a ions is low because he ne wo k is ini ially
2The inaccu acy in oduced by he model gene a ion is impossible o a oid.

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HPC_Max=1 HPC_Max=2 HPC_Max=3
Maximum Mul ihop Leng h
13
14
15
16
17
A g. Fli La ency (Cycles)
N=3
N=5
N=7
N=10
SynFull-RTL-IDEAL
(a) numbe o seeds, N(L= 20 in all cases)
HPC_Max=1 HPC_Max=2 HPC_Max=3
Maximum Mul ihop Leng h
13
14
15
16
17
18
A g. Fli La ency (Cycles)
L=1
L=2
L=5
L=10
L=20
SynFull-RTL-IDEAL
(b) mac o-phase i e a ions, L(N= 5 in all cases)
Fig. 8. Pa ame e analysis o SynFull-RTL using he Ba nes applica ion.
emp y, can be app ecia ed clea ly o small alues; longe
simula ions p og essi ely con e ge. In his case, a high alue
o Lis mo e ele an o accu acy. Using N= 5 seeds and
L= 20 p o ides a e age esul s which di e less han 0.67%
o SynFull-RTL-Ideal.
O iginal SynFull s SynFull-RTL: Figu e 9 compa es he
es ima ion o SynFull-RTL (using N= 5 and L= 20) wi h
he o iginal SynFull, wi h di e en numbe o simula ed cycles
and N= 10 seeds. SynFull needs a highe numbe o seeds
o ob ain a compa able con idence in e al. Fou applica ions
a e selec ed: FFT, Ba nes, Blackscholes and Body ack.
In he ou cases we obse e ha sho simula ions p o-
ide inco ec esul s in SynFull, equi ing many hund ed o
i e a ions o ge close o he esul p o ided by SynFull-RTL.
The esul s wi h sho simula ions di e by up o 15.8% om
he esul p o ided by SynFull-RTL-Ideal. The bias o he
e o depends on he la ency o he ini ial mac o-phase 1. In
FFT, Ba nes and ( o a lesse ex en ) Body ack he la ency o
mac o-phase 1 is high, and sho simula ions o e es ima e he
esul because simula ions always s a he e; in Blackscholes
he bias is he opposi e, o an analogous eason. E en wi h
400M simula ed cycles, he con idence ma gin ob ained wi h
SynFull is clea ly la ge han wi h SynFull-RTL in all cases.
SynFull-RTL a e age la ency is wi hin a 0.58% o he ideal
alue, whe eas SynFull alues wi h 400M cycles di e by up
o 3.2%.
Longe SynFull simula ions could no be un, because hey
exceeded he 2-day ime limi in ou sys ems. By con as ,
he di e en simula ions o L= 20 i e a ions (10M cycles) in
SynFull-RTL can be un in pa allel, educing ime- o-solu ion
by up o 400M/10M= 40×. Wi h hese pa ame e s, SynFull-
RTL simula es N·L= 5 ·20 = 100 i e a ions pe mac o-
phase ( he numbe o mac o-phases anges om 2 o 10 in
he a ailable models). Compa ed o he N·800 = 8000
i e a ions (in he 400M case) in SynFull, compu ing esou ces
a e educed by 8× o 40×, while p o iding mo e accu a e
esul s.
Two aspec s o Ba nes a e ele an . Fi s , sho simula ions
wi h HPCMax = 2 and HPCMax = 3 p esen excessi e
la ency bu educed con idence in e al. This occu s because
he i s mac o-phase has bo h high p obabili y and la ency.
In he “s eady-s a e” execu ion mode (which ends simula ion
p ema u ely when esul s con e ge) his can p oduce inco ec
esul s, because only one mac o-phase is un. Second, he
la ency esul wi h 400M cycles is o e es ima ed, compa ed o
he esul om SynFull-RTL. We ound ha mac o-phase 2 has
a e y low p obabili y (P2= 1.88%) bu a signi ican weigh
(w2= 22.6%) because i injec s abou 9 imes mo e a ic
han he wo o he s. Addi ionally, i has he lowes la ency,
as seen in Figu e 5b. An analysis o ou SynFull simula ions
shows ha i is unde ep esen ed, wi h less han 1% o he
o e all i e a ions. We a ibu e his p oblem o i s low ansi ion
p obabili y and he bias owa ds he esul om mac o-phase
1 because SynFull always s a s om i .
P oSMART e alua ion using SynFull-RTL: Finally, Fig-
u e 10 p esen s SynFull-RTL esul s o all he a ailable mod-
els, cha ac e izing P oSMART. As expec ed, he con idence
in e al is e y na ow. All he applica ions con i m ha
inc easing he mul ihop leng h is p o i able. Addi ionally, he
use o HPCMax = 3 does no yield as much bene i as
inc easing om HPCMax = 1 o 2: In a 4×4mesh, he
occu ence o mul ihops o leng h 3 (i.e. om side o side
o he mesh) is less equen . O e all, a e age li la ency is
educed by 11.3% and 14.8% by inc easing HPCMax o 2 o
3 espec i ely.
VI. RELATED WORK
In his wo k, we use SynFull as a ool o eed an RTL
design. In he p ocess, we de ec ed se e al limi a ions ela ed
o simula ion ime and applica ion ep esen a i eness. Simila
p oblems we e de ec ed in [9], whe e SynFull is used o eed
he e ogeneous sys ems wi h CPU+GPU. In hei case, he
simula ion ime does no allow o explo e all he GPU phases.
The e o e, esul s do no e lec i s highe a ic load. They
modi y he mac o-phases o balance he ela ion be ween he
wo kloads and he ansi ions be ween hem, and hey also
modi y he size o he mac o-phases o include mo e mic o-
phases du ing he execu ion. To gua an ee ha all mac o-
phases occu , hey eco d and eplay he mac o-phase o iginal
o de . In con as , ou SynFull-RTL app oach uses a sampling
solu ion based on a weigh ed a e age ha depends on bo h
s eady-s a e p obabili y and a ic load o each mac o-phase,
wi h mino changes o he SynFull design using. This educes
he need o a la ge numbe o cycles o ob ain eliable esul s.
The o iginal SynFull mechanism [1] al eady conside s
s eady-s a e con e gence. Thei p oposal dynamically analyzes
he con e gence o he pe o mance me ics and e mina es he
simula ion p ema u ely. Howe e , we ha e obse ed pa icula
cases in which his al e na i ely ins ead inc eases he e o o
he esul . Ou app oach elies on he s eady-s a e p obabili ies
o each mac o-phase, de i ed om he ansi ion p obabili ies,
and sampled simula ion.
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5M 10M 20M 50M 100M 200M 400M SynFull-RTL SynFull-RTL-IDEAL
HPC_Max=1 HPC_Max=2 HPC_Max=3
Maximum Mul ihop Leng h
11
12
13
14
15
16
17
A g. Fli La ency (Cycles)
(a) FFT
HPC_Max=1 HPC_Max=2 HPC_Max=3
Maximum Mul ihop Leng h
13
14
15
16
17
18
19
20
A g. Fli La ency (Cycles)
(b) Ba nes
HPC_Max=1 HPC_Max=2 HPC_Max=3
Maximum Mul ihop Leng h
16
17
18
19
20
21
22
23
A g. Fli La ency (Cycles)
(c) Blackscholes
HPC_Max=1 HPC_Max=2 HPC_Max=3
Maximum Mul ihop Leng h
15
16
17
18
19
20
21
A g. Fli La ency (Cycles)
(d) Body ack
Fig. 9. SynFull (blue) and SynFull-RTL (pu ple) esul s o a e age la ency wi h a ying numbe o simula ed cycles (in millions). SynFull-RTL uns each
mac o-phase o 10M cycles using N=5 seeds. Each SynFull ba a e ages esul s om N=10 seeds.
swap ions
cholesky
wa e _spa ial
wa e _nsqua ed
acesim
lu_cb
lu_ncb
adiosi y
blackscholes
ba nes
body ack
luidanima e
ol end
ay ace
adix
a e age
Applica ion Models
12
16
20
24
28
A g. Fli La ency
(Cycles)
HPC_Max=1
HPC_Max=2
HPC_Max=3
Fig. 10. P oSMART a e age li la ency unning he comple e se o a ailable models using SynFull-RTL.
O he wo ks ocus on he SynFull me hodology o cap u e
he a ic bu s in eal applica ions. This is he case o Mock-
ails [10], which employs a simila app oach o ec ea e he
space- ime memo y access beha io in he e ogeneous sys ems
based on memo y IP blocks. This is a e y di e en app oach
o he SynFull-RTL me hodology.
The me hodology in FNoC [11] employs FPGAs o e alua e
ou e models. While he simula ion speed may be highe han
ou app oach, i equi es he de elopmen o complex a ic
gene a ion uni s and is limi ed by he FPGA esou ces.
A s ochas ic me hodology o NoC a ic gene a ion is
in oduced in [12], including a eedback loop ha p opaga es
delays h ough he memo y hie a chy, absen in SynFull. The
applica ion o ou sampling me hodology o his app oach
could be conside ed in he u u e.
VII. CONCLUSIONS
The o iginal me hodology in SynFull p esen s signi ican
limi a ions ha a e analyzed h oughou his pape . The gene -
a ed a ic has a la ge a iabili y and i can be no comple ely
ep esen a i e o he o iginal applica ion being modelled,
because some mac o-phases may be unde ep esen ed o no
execu ed a all in a gi en simula ion, despi e eaching he s a us
o s eady s a e. To mi iga e a iabili y, long execu ion imes
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a e equi ed, and his is exace ba ed when in e acing wi h
complex RTL models.
The me hodology in oduced wi h SynFull-RTL simula es
each mac o-phase in isola ion and a e ages he esul s ac-
co ding o he s eady-s a e p obabili y o occu ence and he
measu ed a ic. While he me hodology can be applied o
adi ional unc ional-le el so wa e simula ions, i has shown
o be pa icula ly ele an o ou RTL modelling app oach, in
which a s aigh o wa d connec ion could exceed he ypical
simula ion imes o many compu e clus e s and delay he
esul s o many days. Indeed, ou me hodology no only
educes he equi ed compu ing esou ces by up o 40×, bu
also educes ime- o-solu ion by up o 40×. Fu he mo e, his
speedup is ob ained wi h inc eased accu acy: we main ain he
e o in e al wi hin a 1%, lowe han he igu e measu ed wi h
he o iginal mechanism.
ACKNOWLEDGMENTS
We would like o hank he anonymous e iewe s o hei
help ul insigh s. We would also hank Miquel Mo e ´
o o help-
ul sugges ions on his wo k. This wo k has been suppo ed
by he Spanish Science and Technology Commission unde
con ac PID2019-105660RB-C22 and he Eu opean HiPEAC
Ne wo k o Excellence. En ique Vallejo has been pa ially
suppo ed by he Minis y o Uni e si ies, Subp og ama Es-
a al de Mo ilidad, g an numbe PRX21/00757. This wo k
also ecei ed unding om he Eu opean Union Ho izon 2020
esea ch and inno a ion p og amme unde g an ag eemen s
numbe 826647 (EPI) and 946002 (MEEP).
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Neiel Ley a is a PhD. S uden a Uni e si a Poli `
ecnica de Ca alunya and
Resea ch enginee a Ba celona Supe compu ing Cen e (BSC). His cu en
esea ch in e es a e Ne wo ks-on-Chip (NoC) design and memo y hie a chies
o many-co e sys ems. He ecei ed he M.S deg ee in Compu e Enginee ing
om Compu ing Resea ch Cen e o he Na ional Poly echnic Ins i u e o
Mexico. neiel.ley [email p o ec ed].
Ali eza Monemi ecei ed he M.S. and Ph.D. deg ees in elec ical enginee ing
om Uni e si i Teknologi Malaysia, Malaysia, in 2011 and 2017, espec i ely.
He is cu en ly a Pos doc o al esea ch associa e a Ba celona Supe compu ing
Cen e (BSC). His cu en esea ch in e es is cache-cohe en Ne wo ks-on-
Chip (NoC) design. [email p o ec ed].
En ique Vallejo is an associa e p o esso in he Uni e si y o Can ab ia
and a isi ing esea che a FORTH. His esea ch in e es s a e in com-
pu e a chi ec u e, mainly in e connec ion ne wo ks and pa allel a chi ec-
u es. He holds a PhD in compu e a chi ec u e om he U. Can ab ia.
en ique. [email p o ec ed].