technische universität münchen we create tools for chips institute for electronic design...
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Technische Universität MünchenTechnische Universität München
We create tools for chips
Institute forElectronic Design Automation
U. Schlichtmann
ISPD 2015 - March 31, 2015
Beyond GORDIAN and KRAFTWERK:EDA Research at TUM
Technische Universität München
Overview
The early years
Plantage: Analog Placement
PROTON: ONoC Physical Design
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Technische Universität München
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Topics covered by Prof. Antreich
• Since 1975: CAD for analog circuits• Early 1980s: Physical Design• Mid-1980s: ATPG• Late-1980s: Digital Simulation (Parallelization)• 1990s: Logic and RTL synthesis (FPGAs)• 1990s: Formal Techniques
Technische Universität München
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Topics covered by Prof. Antreich
• Since 1975: CAD for analog circuits• Early 1980s: Physical Design• Mid-1980s: ATPG• Late-1980s: Digital Simulation (Parallelization)• 1990s: Logic and RTL synthesis (FPGAs)• 1990s: Formal Techniques
Technische Universität MünchenInstitute forElectronic Design Automation
Can you simulate what you want to design?
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Circuit simulation
Simulation:
Performances f
Parameters x
Mathematical View
Physical View
Abstraction from the physical level
Technische Universität MünchenInstitute forElectronic Design Automation
Statistical Parameter Distribution
• Normal distribution of with– mean vector– covariance matrix
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Technische Universität MünchenInstitute forElectronic Design Automation
Operating Range, Performance Specification
• Operating range– Bounds for environmental
parameters
– Simulator input
• Performance specification– Bounds for performances – Simulator output
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Technische Universität MünchenInstitute forElectronic Design Automation
Realistic Worst-Case Analysis
• Linear performance model with gradient for each performance
• Parameter tolerance ellipsoid
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Technische Universität MünchenInstitute forElectronic Design Automation
Relate Yield and Tolerance Region: Worst-Case Distance W
W -1 0 1 2 3 4
Y´ 15.9%
50% 84.1% 97.7%
99.9% 99.99%
sigma design
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Technische Universität MünchenInstitute forElectronic Design Automation
Example – Yield (Worst-Case) Analysis
NominalvaluePerformance
82.9%Overall yield
1.1(„“)/86.4%
2.6 ≤ 3.5DC power [mW]6.3(„“)/99.9%
67 ≥ 32Slew rate [V/s]1.8(„“)/96.9%
68 ≥ 60Phase margin[º]
7.7(„“)/99.9%
67 ≥ 30Transit frequency [MHz]
2.5(„“)/99.4%
76 ≥ 65Gain[dB]
Worst-casedistance/yield
Specifi-cation
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Technische Universität MünchenInstitute forElectronic Design Automation
Worst-case distance
Yieldpartition
50.00%
97.70%
99.99%
Example – Yield (Worst-Case) Analysis
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Technische Universität MünchenInstitute forElectronic Design Automation
Example – Optimized Yield
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1
2
3
4
5
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7
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Initial Optimized
Gain Transit frequency Phase marginSlew rate DC power
50.00%
97.70%
99.99%
Worst-case distance
Yieldpartition
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Empowering Innovation
© Copyright by MunEDA GmbH - All rights reserved - www.muneda.com
MunEDA Corporate Overview
Vision & Mission: Empowering Innovation
EDA Software Vendor – Design Tool Suite WiCkeDTM for IP Migration, Analysis, Modeling & Sizing of Nanometer IC designs
Founded in 2001 - Headquarters in Munich Germany
Worldwide Sales & Support Offices in USA, Korea, China, Taiwan, Japan, UK, Ireland, Scandinavia, South America
Worldwide Customer Base of Semiconductor IDMs, Fabless Design Houses & Foundries
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Empowering Innovation
© Copyright by MunEDA GmbH - All rights reserved - www.muneda.com
MunEDA Worldwide Customers & Foundry Partners (Selection)
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and more…for WiCkeDTM customer references see http://www.muneda.com/Customers
Technische Universität München
The Early Years
Plantage: Analog Placement
Overview
PROTON: ONoC Physical Design
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Technische Universität München
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Analog Design Flow
Specification
Topology
Sizing
Placement
Routing
Layout
Sizing rules
Placement rules
Routing rules
Process variationsParasitics etc.
manual
mostlyautomated
mostlymanual
Technische Universität München
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Modeling: inter-set relations
Placement rules
close proximity
A B
C D
E
symmetry
A B C E
alignment
BA
C
E
minimum distance
A1 B2 A3 B4
B1 A2 B3 A4
commoncentroid
variant constraints
R1
R2
R3
R4
R5
R6
Modeling: linear (in-)equalities
Technische Universität München
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State-of-the-Art
• Two classes of algorithms:– Absolute coordinates
• [Jepsen/Gellat, ICCD‘83]• [Kohn/Garrod/Rutenbar, IEEE Journal SC‘91]
– Topological relations• Sequence Pair [Murata/Fujiyoshi, TCAD‘96],
[Tam/Young/Chu, ICCAD‘06]• BSG [Nakatake/Fujiyoshi, ICCAD‘96]• B*-Tree [Chang/Chang, DAC‘00], [Balasa, TCAD‘04],
[Lin/Lin, DAC‘07], [Lin, DAC‘08]
• Optimization using Simulated Annealing
Technische Universität München
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CM
DS
CM
DP
Hierarchically Bounded Enumeration
• Hierarchical structure of analog circuits
• Automatic structure recognition [Gräb, Zizala, Eckmüller, Antreich ICCAD‘01]
[Massier, Gräb, Schlichtmann DATE‘08]
M5 M6 M7
CM
M1 M2
DP
M3 M4
CMM1 M2
M5 M6 M7
M3 M4
M8
CDS
CORE
OPA
M8 C
CM: Current Mirror
DP: Differential Pair
DS: Differential Stage
Technische Universität München
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Enumeration of placements of fundamental module sets
Hierarchically Bounded Enumeration
M5 M6 M7
CM
M1 M2
DP
M3 M4
CM
DS
CORE
OPA
M8 C
Fundamental
module sets
M5 M6 M7
M5
M6
M7M5
M6M7
…
M3 M4
M3
M4M3
M4
…
M8 C
M8
C
M8
C
…
M1a
M2a
M2b
M1b
…
M2
aM
1a
M1
bM
2b
M5 M6 M7
CM
M1 M2
DP
M3 M4
CM
DS
CORE
OPA
M8 C
Technische Universität München
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Overview
M5 M6 M7
CM
M1 M2
DP
M3 M4
CM
DS
CORE
OPA
M8 C
M5 M6 M7
M5
M6
M7M5
M6M7
…
M3 M4
M3
M4M3
M4
…
M8 C
M8
C
M8
C
…
M1a
M2a
M2b
M1b
…
M2
aM
1a
M1
bM
2b
• Hierarchical approach• Enhanced shape functions• New algorithm for placement:
B*-Tree CG LP• New addition of shapes
by addition of B*-Trees
CM
DS
CM
DP M1 M2
M5 M6 M7
M3 M4
M8
C
Enhanced Shape Functions
Enhanced Shape Addition
Technische Universität München
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Results
• OTA with folded cascode
#10
12 shapes in 44 sWSCM: Wide Swing Cascode current MirrorDP: Differential Pair
DP
WSCM #3optimum
area
#8
DP
WSCM
Technische Universität München
The Early Years
PROTON: ONoC Physical Design
Overview
Plantage: Analog Placement
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Technische Universität München
The Architecture
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TSV
TSV
TSV
TSV
M1
2
3
4
λ1
λ2
λ3
λ4
Array of off-chip CW lasers
Electronic layer
Photonic layer
Off-chip memory
Clusters of processors
M2
M3M
4H4
H3
H2
H11
Technische Universität München
The Photonic Layer
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M1M1
Photonic layer
M2M2
M3M3
M4M4
H4
H3
H2
H1
M2M2 H3
• A path connects two Hubs or a Hub and a Memory Controller via Photonic Switching Elements (PSEs) and waveguides
Memory Controller PSE PSE Hub
λi λj
Technische Universität München
Example: Logic Scheme of Optical Layer
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Example: 8x8 λ-Router [Scandurra NoCArc’08]
λ1
I1 O1
O2
Wavelength λ1 is redirectedWavelength λn crosses
λ4
I2
Technische Universität München
Logic Scheme vs. Physical Design
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H1
H2
H3
H4
M1
M2 M4
M3
Logic Scheme Manually created layout
Creating layout manually is time consuming, error prone and suboptimal
PROTON: The first automatic place & route tool for 3D optical NoCs
[Ramini NOCS’12]
Technische Universität München
Placement and Routing Problem
• Minimize maximum insertion loss over all paths, e.g. minimize– Waveguide length– Number of crossings between
waveguides– Number of bends
• Constraints:– Place all PSEs and waveguides
inside chip area– No overlap
Netlist, chip area, positions of hubs and memory controllers
Valid and optimal layout
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Technische Universität München
Placement and Routing Algorithm
RoutingRouting
• Approximate waveguide length of each path by using a quadratic net model
• Approximate number of crossings
• Solve non-linear optimization problem
• Route waveguides by allowing but penalizing crossings
• E.g. Maze Router
PlacementPlacement
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Technische Universität München
Approximation of Crossings
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Module r
Module s
Module i
Module j
• Approximated number of crossings is calculated with the weights of Simpson’s rule
Technische Universität München
Solving the Optimization Problem
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• Find positions of all PSEs
• Constraints– Place all PSEs inside chip area– No overlap between optical devices (PSEs, hubs, memory
controllers)
• Nonlinear Optimization Problem solver: IPOPT (Interior point method)
Technische Universität München
Contributions & Acknowledgements
Anja von Beuningen Helmut Graeb Martin Strasser Michael Eick Frank Johannes
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