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Large-Scale Optimization in VLSI CAD. Igor Markov http://www.eecs.umich.edu/~imarkov. Goals/Outline of the Talk. Give a general idea about the field success stories and applications potential for cross-pollination What drives the field Reusable Intellectual Property in CAD

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Large scale optimization in vlsi cad

Large-Scale Optimizationin VLSI CAD

Igor Markov

http://www.eecs.umich.edu/~imarkov


Goals outline of the talk
Goals/Outline of the Talk

  • Give a general idea about the field

    • success stories and applications

    • potential for cross-pollination

  • What drives the field

  • Reusable Intellectual Property in CAD

  • Consequences of “large-scale”

  • Sample wide-open problems

Igor Markov, U. of Michigan


General vlsi cad
General (VLSI CAD)

  • Very Large System Integration

    • numerous components + interconnect

    • emergent properties

      • not apparent in isolated components

  • Computer-Aided Design

    • better than human design (super-human!)

    • and then some

FOR MORE INFO...

http://www.eecs.umich.edu/~imarkov/EECS527

Igor Markov, U. of Michigan


Integrated circuits
Integrated Circuits

  • Excellent examples of “large systems”

    • manufacturing is enormously expensive

      • research can prevent blunders… and pays off

    • two Moore’s laws keep everyone busy

      • circuits are growing

      • circuit design is getting harder

    • decreased market windows

      • must design quickly (or else…)

    • digital circuits amenable to auto- manipulation

      • have a lot of regularity (easier to represent)

Igor Markov, U. of Michigan


Just how large
Just How Large?

  • As large as we can handle

    • a priori (physical) limitsare at least 20 years away

    • pushing the boundaries is ourgoal

  • Current limits

    • need to solve many NP-hard problems

    • poor understanding, mathematical models

    • lack of efficient algorithms

  • (typical problem sizes will follow)

Igor Markov, U. of Michigan


Design via optimization
Design via Optimization

  • Think of all possible design solutions

    • “solution space”

    • need to choose one solution (or several)

  • What parameters should be optimized?

    • “objective functions” f1(x), f2(x),…

  • Need to observe design constraints

  • The EDA revolution of the 1980s:

    • searching, combinatorial and mathematical optimization may outperform engineeringintuition when implemented in software

Igor Markov, U. of Michigan


A meta approach to optimization
A Meta-Approach to Optimization

  • Global Optimization

    • often cannotoptimize “accurate” objectives

      • they can be hopeless to evaluate

      • e.g., min routed wirelength as f(placement)

    • find simpler objectives that correlate well

    • ditto for constraints

  • Detailed Optimization

    • improve global solutions by local search

      • can now worry about weird constraints

      • can optimize a better measure of signal delay, etc

Igor Markov, U. of Michigan


Consequences of large scale
Consequences of “Large-Scale”

  • Runtimes must scale near-linearly

    • strict limitation on used primitives(e.g., no Gaussian elimination)

    • wide-spread use of multi-level methods

  • Same goes for memory consumption

    • cannot represent graphs as dense matrices

    • use random sampling/walks instead of enumeration

  • Trading solution quality for runtime

    • especially for randomized algorithms

Igor Markov, U. of Michigan


Historic opportunism
Historic Opportunism

  • In early days of VLSI CAD…

    • the Electronic Design Automation revolution

      • enabling, but short-lived results (can easily do better)

      • e.g., “this new algorithm addresses objective f(x)”

    • many proposed approaches never picked up

  • As ICs became larger, most CAD toolscould not handle leading-edge circuits…

    • “algorithms for Deep SubMicron circuits”

    • soon turned out that many algos were weak

      • partitioning, placement, SAT, etc.

Igor Markov, U. of Michigan


Competitiveness
Competitiveness

  • Outdated algorithms cause costly software rewrites and lost opportunity

    • commercial tools may sell for $400,000+

  • Learningcircuit physics, optics, semiconductor technologies, applied math, CS theory, AI, databases, proper software design, etcis well worth the effort

    • competitive edge

  • As a result of competitiveness, VLSI CAD offers

    • some of the best algorithms, very strong implementations

    • frequent contributions to other fields

Igor Markov, U. of Michigan


Success stories
Success Stories

  • Min-cut [hyper-] graph partitioning

    • (“very good” solutions)

    • 200K 0/1 variables, 1-2 mins of CPU time

  • Minimal Steiner trees (optimal)

    • hundreds of points in 1 second

  • Provably good routing (approximation)

    • 500K nets in several hours (!!!)

Igor Markov, U. of Michigan


Min cut partitioning
Min-cut Partitioning

  • Given

    • [hyper-] graph

    • k bins

      • each accommodates up to N vertices

  • Seek

    • to assign each vertex to a bin

  • Minimize

    • # of [hyper-] edges between bins

Igor Markov, U. of Michigan


Min cut partitioning cont d
Min-cut Partitioning (cont’d)

  • Numerous apps in VLSI CAD + beyond

    • supercomputing, data mining, Internet,…

  • Progress in partitioning algorithms

    • started in 1972 and still going

      • many approaches invented / discarded

    • now can auto-partition 1M-gate circuits

      • better than manually, with free software

      • couldn’t, even commercially, just 3 years ago

      • (this has nothing to do with Deep SubMicron)

Igor Markov, U. of Michigan


Min cut partitioning cont d1
Min-cut Partitioning (cont’d)

  • UCLA MLPart(ASPDAC 2000)

    • faster than hMetis per start

    • returns better solutions on average

    • never worse than5% offfrom hMetis

    • sometimes (ibm06,2%aa) 30% better

    • available in source code (C++) and binaries

      • at “the bookshelf”, free for any use w/o notification

  • Used at Cadence, Intel, start-ups

  • Vital to UCLA Capo placer

Igor Markov, U. of Michigan


Steiner minimal trees
Steiner Minimal Trees

  • Given

    • k points in the plane

  • Seek

    • a Steiner tree connecting the points

      • add extra points

      • connect all points by straight-line segments

  • Minimize

    • total edge-length of the tree

Igor Markov, U. of Michigan


Steiner minimal trees cont d
Steiner Minimal Trees (cont’d)

  • Applications

    • routing signal nets

    • connecting cities by highways

  • 1989, Scientific American

    • “cannot find an SMT for 100 US cities”

  • 1999, SODA (Warme/Zachariasen)

    • with GeoSteiner can do that in <1sec

    • implementation available in source code

Igor Markov, U. of Michigan


Routing of multiple nets
Routing of Multiple Nets

  • Given

    • n-tuples of locations to be connected

      • with Steiner trees (think of signal nets)

  • Constraints (not trivial to satisfy!)

    • routes cannot occupy same space

  • Minimize

    • total length of routes, “congestion”

Igor Markov, U. of Michigan


Routing of multiple nets1
Routing Of Multiple Nets

  • One of the first circuit design automations (late 1960s)

  • Has enormous solution space

  • A classic AI problem

  • Current commercial tools (e.g., Cadence)

    • up to a day for 500K nets, no guarantees

  • ISPD 2000, Albrecht (using multi-commodity flows)

    • 500K nets in several hours, within 20% of opt.

    • (IBM Power 3 chip)

Igor Markov, U. of Michigan


What makes a break through or at least a splash
What Makes a Break-through?(or at least a splash)

  • Study sample splashes

  • Is it enough to minimize a function? (function - relevant, minimization - efficient)

    • Yes

    • Yes, but …

    • No

    • Absolutely not

Igor Markov, U. of Michigan


Background vlsi placement
Background: VLSI Placement

bad placement good placement

Igor Markov, U. of Michigan


Global wl driven placement
Global WL-driven Placement

  • Objective

    • total Half-Perimeter WireLength

    • approximates Steiner Minimal Tree

  • UCLA Capo placer(DAC 2000)

    • beats Cadence QPlace on many benchmarks

      • <50k gates; unpublished: 30% better on a 280K gate bm.

      • compared by “routed WL” after Cadence WarpRoute

      • in congestion-driven mode; 1 routing violation = failure

    • used for research at IBM, Intel, Phillips; CMU,…

    • available in source code (C++), free for any use

    • (timing-driven mode not yet released)

Igor Markov, U. of Michigan


Background detailed placement
Background: Detailed Placement

  • Detailed circuit placement

    • given locations of circuit elements (“cells”),improve them by local changes (e.g., swaps)

    • minimize total length of signal nets

  • “Local”, but large-scale problem

    • entails a very large number of small sub-problems

  • Practically important

    • local improvements directly translate to large scale

    • very similar to floorplanning (a high-level problem)

Igor Markov, U. of Michigan


Background detailed placement1
Background: Detailed Placement

  • Naïve “detailed” optimization

    • consider 7-8 “cells” at a time

    • enumerate all permutations

    • compute HPWL for each

    • pick the best permutation

    • repeat for another group of 7-8

  • Greater groups  better solutions

    • practical limit: 0.01sec per group

  • Use Branch-and-bound for each group (ISPD `99)

  • Overall linear runtime

  • Easy parallelization (optimize many groups in ||)

Igor Markov, U. of Michigan


Optimal interleaving
Optimal Interleaving

  • Can handle 30+ elements at a time

    • easier to implement than B&B

    • the order constraint turns out very mild

  • Very good result

    • but, seemingly, nothing more than min f(x) !

  • ICCAD 2000, Hur and Lillis (TR available)

A

B

C

D

E

1

2

3

4

5

Optimally inO(n2) time

by Dynamic Programming

A

1

2

B

C

3

4

D

5

E

Igor Markov, U. of Michigan


Popularity comparison w geosteiner
Popularity Comparison w GeoSteiner

  • The Hur/Lillis algorithm

    • appeared several months ago (on paper)

    • already implemented by several groups

      • with great results

  • … but Warme’s GeoSteiner

    • is barely used

    • source code published 2 years ago

    • instead, used are simple heuristics that are slower

  • Difference: ease of reuse!

    • of result itself and/or of its representation

Igor Markov, U. of Michigan


Intellectual property in cad
Intellectual Property in CAD

  • Reuse?

    • today hundreds of VLSI CAD engineersare implementing the same, known, but difficult algorithms

  • Breakthroughs typically producevalidated and reusable intellectual property

    • yet another algorithm to min f(x) does not automatically qualify for validated, reusable CAD IP

      • applicability, generality, quality of description, etc.

    • CAD IP is not just algorithms and code

    • CAD IP: benchmarks, evaluation techniques, empirical studies/results, algorithm analyses,etc

  • Studies of CAD IP suggest:

    • to effectively reuse, need infrastructure

Igor Markov, U. of Michigan


Intellectual property in cad1
Intellectual Property in CAD

  • GRSC Bookshelf for Fundamental Algorithms in CAD

    • a repository for reusable CAD IP, a publication medium

    • a way to communicate with industry

      • problem formulations are also considered CAD IP

    • http://vlsicad.cs.ucla.edu/GSRC/bookshelf

  • Existing bookshelf “slots” include

    • SAT, Graph Coloring, Hypergraph Partitioning, Mathematical Optimization, Circuit Placement, Clock Tree Routing, Global Routing, Interconnect Optimization, etc…

  • Leading-edge implementations (free for all uses)

    • UCLA Physical Design Tools (graph partitioners, placers,etc)

    • many more (SAT solvers from U. Michigan, GeoSteiner, etc)

Igor Markov, U. of Michigan


Reuse and education
Reuse and Education

  • Both are necessary to sustain Moore’s laws

    • not enough designers to implement new chips

    • not enough CAD engineers to automate design

  • Need to teach/studyreusable design

    • hardware, software/CAD IP (similar? different?)

    • note: typical “promising” research demos not reusable

  • Design of reusable software

    • “theory” has been available for years (processes, code metrics, interface languages, modeling, robust public-domain tools, etc)

    • need [more] infrastructure, practice, experience of reuse

    • first: reuse software

    • then: design reusable software

Igor Markov, U. of Michigan


Research directions 1
Research Directions (1)

  • “Citius, Altius, Fortius”

    • faster, leaner implementations

    • higher-quality solutions

    • stronger impact on applications

    • aid available: latest advances in CS theory, Mathematics, AI, software engineering, etc

  • Large-scale computing aspects of VLSI CAD

    • memory locality (big deal for irregular circuits)

    • “memory-less” algorithms (and trade-offs)

Igor Markov, U. of Michigan


Research directions 2
Research Directions (2)

  • Quantified suboptimality of heuristics

    • (for NP-hard problems)

    • how close can we get to optima in practice?

      • estimate suboptimality of specific solutions

      • study dependence on input distributions

      • related to CS theory / approximation algos

    • example: detection of symmetries in Logic Synthesis

      • Kravets/Sakallah, ICCAD 2000 and TR

  • Lower bounds and impossibility arguments for fundamental algorithms

Igor Markov, U. of Michigan


Research directions 3
Research Directions (3)

  • Using better, but still computable, models of reality

    • simulation as a driver for optimization

    • modeling semiconductor effects

      • Alpert et al, ISPD 2000 --- a new interconnect delay model, better than Elmore delay; all optimizations assuming Elmore are open to “porting”

      • inductance, noise, etc

    • effects of statistical variations

  • CAD for new types of semi technologies and styles

    • subwavelength lithography (optical proximity correction, etc)

    • System-On-Chip (high-level partitioning, etc)

  • CAD for analog circuits (including RF, MW)

Igor Markov, U. of Michigan


Research directions 4
Research Directions (4)

  • Self-conscious optimization tools

    • prediction and estimation

      • of solution quality before optimization

      • SLIP 2001 - http://www.ee.pdx.edu/~slip

      • GTX -http://www.gigascale.org/gtx

    • calibration (which solutions/tools are good?)

  • Support for intelligent/expert users

    • “computer-aided” does not always mean “w/o people”

    • efficient visualization, diagnostics and interactivity

      • how do you visualize a partitioning solution?

      • how do you visualize many unrouted 2-pin netsin same row?

Igor Markov, U. of Michigan


Conclusions
Conclusions

  • Large-scale optimization in VLSI CAD

    • dynamic and challenging field

    • benefits from other fields and gives back

    • IP reuse is paramount

    • research is respected and economically justified

    • opportunities available

FOR MORE INFO...

http://www.eecs.umich.edu/~imarkov/

Igor Markov, U. of Michigan


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