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Computational Intelligence Techniques for Electronic Design Automation
Bo Liu
Department of Computing, Glyndwr University, UK
ESAT-MICAS, Katholieke Universiteit Leuven, Belgium
(Bo.Liu@esat.kuleuven.be, b.liu@glyndwr.ac.uk)
Outline Electronic design and optimisation
Evolutionary algorithms in EDA: How it works and challenges Surrogate model assisted evolutionary algorithms (SAEA)
The SMAS framework, improvements and applications:
Medium-scale problem: automated design of complex antennas Constraint handling: automated design of mm-wave ICs Integer optimisation: NoC design optimisation Challenges and opportunities
Conclusions
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Microelectronic design
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Analog
IC
Digital IC
Antenna
RF IC
MEMS
Design variables: W, L of each transistor, Cc
For the goal of (such as):
They are optimisation problems
Computational Intelligence (CI)-based EDA
Traditional Design Process and Challenges
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Idea of CI-based EDA approaches:
TRANSFORM difficulties on “analysis, intuition and inference” TO difficulties on solving mathematical optimisation problems
Why CI is needed?
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• Properties of EDA problems: • Multimodal and global optimisation is necessary
• They are simulation-based optimisation, while analytical equations are often unavailable
• The simulation may cost a long time
• CI-based EDA approaches aims at obtaining highly optimised designs (better than manual design) automatically in a practical time by: • Evolutionary computation (EC)
• Machine learning (ML)
Outline Electronic design and optimisation
Evolutionary algorithms in EDA: How it works and challenges Surrogate model assisted evolutionary algorithms (SAEA)
The SMAS framework, improvements and applications:
Medium-scale problem: automated design of complex antennas Constraint handling: automated design of mm-wave ICs Integer optimisation: NoC design optimisation Challenges and opportunities
Conclusions
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Evolutionary Computation (EC)
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• Evolutionary computation is a CI method for optimisation
• EC is based on natural selection, survival of the fittest (objective function) • EC has strengths on black-box and multimodal problems
• Different global optimization algorithms: GA, DE, PSO, IA, AC
Initialize population
Evaluation Fitnees
Select Survivors
Crossover
Mutation
Convergance?
Output Result
Yes
No
CompetitionRanking Based
SelectionRoulette wheelTournament
ReproductionOne point, Two point, Uniform, SBX, Linear, etcBinary, Uniform, Gaussian
How EA Works for EDA Problems?
Simulators: IC: Cadence Virtuoso, Synopsys HSPICE, … Electromagnetic device / antenna: Momentum, CST, SONNET, … MEMS: CoventorWare, MEMS+, COSMOL, … Energy system: AEPS, Energy+, …
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Example: Analog IC Sizing (1)
ARCHITECTURE/
TOPOLOGY SELECTION
CIRCUIT SIZING
LAYOUT GENERATION
VERIFICATION
DRC+EXT+LVS
RE
DE
SIG
N L
OO
P
VERIFICATION
SPECS
M1 M2 vinvip
M13
vvcn
M2CM1C
M3C M4C
vvcp
M3 M4
VSS
M6 M12
M8 M10
cln
VDD
cc
M5 M11
M7M9
clp
cc
Mbp
ibb
Mbn
vonvop
min . . 80
250 60 4
4......
area
s b DC gain dB
GBW MHz
phase margin
output swing V
power mW
• Optimise Ws, Ls, Cc, Ibb
• No initial design
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Example: Analog IC Sizing (2)
B. Liu, F. Fernández, G. Gielen, R. Lopez, E. Roca, "A Memetic Approach to the Automatic Design of High-Performance Analog Integrated Circuits", ACM Transactions on Design Automation of Electronic Systems, vol. 14, no. 3, pp. 1-24, 2009.
• Two-stage telescopic cascode amplifier sizing with tight constraints (high specifications)
Time: 20 minutes
CI-based EDA : Investigation & Application
EA-based design automation has been investigated and applied Academic:
IEEE: TCAD, TCAS, TVLSI, MTT, TAP, … ACM: TODAES, … Elsevier: Integration journal, Microelectronics journal, … …
Industry:
Cadence Neocircuit: Analog circuit sizing MunEDA WiCkeD: variation-aware analog circuit sizing SolidoDesign: various software for handling process variations …
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Main Challenges
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• Main challenges:
• Robust design
• System synthesis method: from block to system
• Long simulation time: impractical optimisation time • Analog IC with standard process parameters -> < 2 seconds /
simulation
• Yield estimation of analog IC -> a few minutes / simulation
• RF / mm-wave circuit -> 10-20 minutes / simulation
• Antenna / MEMS / NoC: various from 5 minutes to several hours / simulation
• Evolutionary algorithms often needs several hundreds to thousands of simulations to achieve highly optimised solutions.
Example: On-chip antenna design optimisation:
EM simulation for a candidate design: 20 minutes by ADS-Momentum
Convergence: 800 generations
Population size: 40
20min x 40 x 800 = 1.2 years!!!
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Example
B. Liu, H. Aliakbarian, Z.Ma, G. Vandenbosch, G. Gielen, "An Efficient Method for Antenna Design Optimization Based on Evolutionary Computation and Machine Learning Techniques", IEEE Transactions on Antennas & Propagation, vol. 62, no. 1, pp. 7-18, 2014.
Solutions to the efficiency problem
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Efficiency enhancement
Evolutionary algorithms
Speed up the simulation
Use fewer simulations
SAEA
Change the optimizer
Outline Electronic design and optimisation
Evolutionary algorithms in EDA: How it works and challenges Surrogate model assisted evolutionary algorithms (SAEA)
The SMAS framework, improvements and applications:
Medium-scale problem: automated design of complex antennas Constraint handling: automated design of mm-wave ICs Integer optimisation: NoC design optimisation Challenges and opportunities
Conclusions
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Introduction to SAEA
Surrogate model assisted evolutionary algorithm (SAEA): Using surrogate models to replace exact function evaluations
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Surrogate modelling, Prediction and Prescreening
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Ordinary GP modeling
Given training data:
Correlation function:
Maximize likelihood function:
Note: solve in closed form, estimate the hyper-parameters
Best linear unbiased prediction and predictive distribution
variants: simple/blind/…
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Prescreening
• With the uncertainty measurement, we can consider the quality of a candidate design in a global picture
• Even the predicted value is bad, promising solutions can still be discovered
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D. Jones, 2001. “A Taxonomy of Global Optimization Methods Based on Response Surfaces”, Journal of Global Optimization, pp. 345-383.
Prescreening
• Prescreening methods utilize
the prediction uncertainty.
• Possible promising areas but with less training data can be effectively explored.
•The “guessed” promising points
may not be correct.
• In medium scale (20-30d), prescreening ≈ prediction
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A Key Contradiction
Model quality Solution quality
Efficiency *x evalN
How to perform effective global optimisation without high quality surrogate model(s)?
Where are optimal locations of the samples?
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Brute force, in-fill sampling, SAEA (1)
Brute force off-line surrogate modelling High quality model but too
time consuming
In-fill sampling techniques (EI, PI, LCB, etc) Design space understanding vs.
Optimisation? More than 15-20 dimensions? Do we need understanding of
the whole space or a single road is OK?
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Most available SAEAs
Brute force, in-fill sampling, SAEA (2)
SAEA EA search -> the next sampling point Dozens of variables: EDA problems
EA search pattern Model quality? Most available SAEAs 20-50
dimensional problems still use many exact function evaluations (5000-50000)
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Most available SAEAs
Outline Electronic design and optimisation
Evolutionary algorithms in EDA: How it works and challenges Surrogate model assisted evolutionary algorithms (SAEA)
The SMAS framework, improvements and applications:
Medium-scale problem: automated design of complex antennas Constraint handling: automated design of mm-wave ICs Integer optimisation: NoC design optimisation Challenges and opportunities
Conclusions
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Surrogate Model-Aware Evolutionary Search Framework
A new unified method of surrogate modelling and evolutionary search
•Not using more samples, but control the locations of samples, for the objectives of:
• Good prediction accuracy • Effective search mechanism
B. Liu, Q. Zhang, G. Gielen, "A Gaussian Process Surrogate Model Assisted Evolutionary Algorithm for Medium Scale Expensive Black Box Optimization Problems", IEEE Transactions on Evolutionary Computation (In Press).
25
SMAS vs. Present SAEA
SMAS Traditional SAEA
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Advantages of SMAS (1)
Property 1: Using the current best candidates as the parent population.
Advantage 1: Optimal solutions are not far away from each other, so a good surrogate model with much fewer samples can be constructed.
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Property 2: Only at most one candidate is different from two consecutive parent populations.
Advantage 2: The training data describing the current search region can be much denser.
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from child population
Advantages of SMAS (2)
Convergence property of SMAS
For a standard EA, some diversity are useful, while some are not.
SMAS emphasizes exploitation.
The exploration ability can be maintained by selecting appropriate EA operators and parameters.
Simulation: 20-dimensional Ackley function (assuming absolutely
accurate model)
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B. Liu, Q. Zhang, G. Gielen, “Behavioral Study of the Surrogate Model-aware Evolutionary Search Framework”, in Proceedings of IEEE World
Congress on Computational Intelligence, 2014 (In Press).
Optimisation Kernel: Differential Evolution
,1 ,2 ,ˆ ( ) [ , , , ]
i i i MX t x x x 1, 2, , NPi
,,
,
( 1), ( ( ) ) ( ),( 1)
( ), , 1, 2, , i j
i j
i j
v t if rand j CR or j randn iu t
x t otherwise j M
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Experimental Verifications (1)
Problems: 20-,30-dimensional Ellipsoid (F1-F3, opt:0), Rosenbrock (F4-F6, opt:0), Ackley(F7-F9, opt:0), Griewank(F10-F12, opt:0), 30-dimensional RS-Rastrigin(opt: -330), 30-dimensional RH composition function(opt:10). 1000 evaluations, 20 runs.
SMAS vs. GS-SOMA [Lim IEEE TEVC 2010]
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Experimental Verifications (2)
SMAS vs. SAGA-GLS [Zhou IEEE TSMC 2007]
SMAS vs. MAES [Emmerich IEEE TEVC 2006]
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Automated Design of Complex Antennas (1)
• EAs have been widely used for antenna synthesis, but the long optimisation time largely limits their applications
Example: Four-element antenna array (3.4GHz – 3.8GHz, FR4 substrate)
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Automated Design of Complex Antennas (2)
• Maximise realised gain (each sampling point at least 13dB) with S11 below -10dB
Synthesis finished in only one night, with 71.05dB (5 sampling points total) realised gain
Comparable solutions, 3-7 times speed enhancement compared to DE, PSO.
RF IC / mm-wave IC Design Difficulties for manual design
Passive component design is difficult, although circuit configurations are
simpler than analog IC Long simulation time: EM simulation, HB simulation Design experience intensive for the available step-by-step manual design
method
Difficulties for EA-based automated design
Accurate lumped models (computationally cheap) over a wide bandwidth for passive components are difficult to find at high-frequencies
Long simulation time: EM simulation, HB simulation Medium scale (15-40 variables) Complex and tight constraints
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Review of RF IC Design Automation
Existing method: equivalent circuit model for passive components, only
applicable to less than 10GHz RF design automation. [Allstot2003Springer] [Ramos2005TCAS ]
mm-wave frequency / general: No design automation method before our work [Liu IEEE TCAD 2012/2014]
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The GASPAD Method Focuses on 60GHz and above RF IC
Three Main Goals of the Synthesiser Provide Highly Optimised Results General Enough to Any Circuit Configuration, Any Technology and Any
Frequency Efficient Enough for Practical Use
Update SMAS on constraint handling
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B. Liu, D. Zhao, G. Gielen, "GASPAD: A General and Efficient mm-wave Integrated Circuit Synthesis Method Based on Surrogate Model Assisted Evolutionary Algorithm", IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 33, no. 2, pp. 169-182, 2014.
Updating SMAS for Constraint Handling
New methods to model the
focused search region
New ranking methods
considering constraint
satisfaction
Separate modelling
objective and constraints
Prescreening + predicted
value
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mm-wave IC Design Automation (1)
Synthesis of a 60GHz power amplifier in a 65nm CMOS technology (18 parameters)
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15 min / simulation
mm-wave IC Design Automation (2)
Design parameters and their ranges
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Synthesised Result
Synthesised results: Power added efficiency (@P1dB): 9.85% 1dB compression point: 14.87dBm Power gain: 10.73dB K factor: 10.68 (stable)
About 2 days synthesis time. Much better performance than manual design [He 2010 RFIC]
The first method of general mm-wave IC design automation
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Expensive EDA Problems with Integer Variables
• Integer design variables lead to discontinuous landscapes, which is a challenge for:
– Surrogate modelling
– Evolutionary search
• NoC design optimisation
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Updating SMAS for Handling Integer Variables Parameter setting and search strategy selection rules:
Scaling factor Self-adaptive crossover rate DE/ctb/1 strategy
Iterative surrogate-assisted neighbourhood exploration method Aims:
Jump out of local optima Direct improvement Assist the SMAS flow
Methods: A self-adaptive perturbation method Local surrogate modelling Greedy search + Opposite DE search
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NoC Design Parameter Optimisation
NoC performance
Design parameter opt
A single simulation costs 15 min to 1 hour for 15x15 to 30 x 30 NoC
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Architecture (Baseline, VCT, Hybrid, etc.)
Design parameters (Number of virtual channel, buffer depth of the router, etc.)
Application specific (a single loading environment) More general purpose (various loading environments)
Application Specific NoC optimisation
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Optimisation of a 15 x 15 NoC with hybrid architecture with the environment of PIR=0.018, PS1=2, PS2=12, BR=0 and hotspot nodes at 35, 57, 108, 155, 175 with 3%, 3%, 2%, 2%, 3% (random traffic)
1 1 2 2 5 5
1 1 2 2 5 5
2
minimize ( , , , , , ,..., , )
s.t. ( , , , , , ,..., , ) 0.16
area 850mm
c p
c p
D N S X Y X Y X Y
E N S X Y X Y X Y J
2
37 hours optimisation55.780.153
805.14
D cycles
E J
area mm
[1,10], [1,12], [1,8], , [1,15] (all integers)c pN S B X Y
NoC Design for General Purpose Dozens of typical loading environments need to be
considered: Example: CMP with cache coherence protocols: 80 combinations of typical broadcast ratio, packet size and
packet injection rate
8 x 8 NoC optimisation for CMP with cache coherence protocols has been addressed, but 15 x 15 NoC, 30 x 30 NoC?
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Prohibitively expensive simulation
A Few Challenges and Opportunities (1) Computationally prohibitively expensive
optimisation More than 1 hour / simulation Dozens of design variables Complex landscapes
EDA problems General purpose large dimensional NoC design optimisation Some problems in MEMS design optimisation 3D complex antenna design Some problems in optical devices design optimisation …
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A Few Challenges and Opportunities (2) Computationally expensive multiobjective
optimisation
EDA problems: almost every problem
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B. Liu, Q. Zhang, F. Fernández, G. Gielen, "An Efficient Evolutionary Algorithm for Chance-constrained Bi-objective Stochastic Optimization and Its Application to Manufacturing Engineering", IEEE Transactions on Evolutionary Computation, vol. 17, no. 6, pp. 786-796, 2013.
A Few Challenges and Opportunities (3) Possible starting points: A system utilising various electronic elements (IC,
MEMS, antenna, optical device, etc) Example: bio-medical system, energy harvesting system
Starting from computational expensive blocks Example: MEMS
From block synthesis to system synthesis
Co-design of hardware and algorithms
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Outline Electronic design and optimisation
Evolutionary algorithms in EDA: How it works and challenges Surrogate model assisted evolutionary algorithms (SAEA)
The SMAS framework, improvements and applications:
Medium-scale problem: automated design of complex antennas Constraint handling: automated design of mm-wave ICs Integer optimisation: NoC design optimisation Challenges and opportunities
Conclusions
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Conclusions (1) Handling expensiveness is unavoidable for future (intelligent) EDA
tools in many applications.
In most present SAEAs, the surrogate modelling and the evolutionary search are loosely cooperated.
New SAEA frameworks (unconstrained / constrained continuous / discrete optimisation) are presented, showing significant improvements in terms of efficiency while keeping the high solution quality. Some computationally intractible EDA problems can therefore be solved.
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Conclusions (2) CI methods based EDA tools
is possible to have a large impact for electronic design (IC, MEMS, antenna, optical devices, energy systems, etc) in the coming future.
Engineers with a new knowledge structure are needed.
Promote this emerging multidisciplinary research and education.
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M. Fakhfakh, E. Cuautle,
P. Siarry
Computational Intelligence in Electronic Design Springer, 2015
Acknowledgments
Sincere thanks to Prof. Georges Gielen, Prof. Francisco V. Fernandez, Prof. Alex Yakovlev, Dr. Patrick Degenaar, Prof. Qingfu Zhang, Prof. Guy Vandenbosch, Prof. Tom Dhaene, Prof. Vic Grout, Dr. Hao-ming Chao, Mr. Dixian Zhao, Mr. Ammar Karkar Mr. Noel Deferm, Dr. Brecht Machiels, Miss Ying He, Mr. Mengyuan Wu, Mr. Bohan Yang, Miss Borong Su, Miss Wan-ting Lo for their valuable help!
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Thank you
Thank you!
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