By Rolf Drechsler
In VLSI CAD, tough optimization difficulties must be solved on a relentless foundation. numerous optimization suggestions were proposed long ago. whereas a few of these tools were proven to paintings good in functions and became a little confirmed through the years, different innovations were missed.
lately, there was a turning out to be curiosity in optimization algorithms according to ideas saw in nature, termed Evolutionary Algorithms (EAs).
Evolutionary Algorithms in VLSI CAD offers the fundamental options of EAs, and considers the applying of EAs in VLSI CAD. it's the first publication to teach how EAs can be used to enhance IC layout instruments and strategies. a number of profitable functions from assorted components of circuit layout, like good judgment synthesis, mapping and trying out, are defined intimately.
Evolutionary Algorithms in VLSI CAD comprises components. the 1st half discusses simple ideas of EAs and offers a few easy-to-understand examples. in addition, a theoretical version for multi-objective optimization is gifted. within the moment half a software program implementation of EAs is equipped including precise descriptions of numerous EA functions. those functions disguise a variety of VLSI CAD, and diverse tools for utilizing EAs are defined.
Evolutionary Algorithms in VLSI CAD is meant for CAD builders and researchers in addition to these operating in evolutionary algorithms and methods helping smooth layout instruments and processes.
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Extra info for Evolutionary Algorithms for VLSI CAD
The size of the population IPI is adapted dynamically using problem specific measures. ) Initialization At the beginning of each EA run an initial population is randomly generated and the fitness is assigned to each element. e. to use REAs. For FPRM minimization the REA makes use of the following (simple) greedy heuristic from : Greedy Heuristic (GRE): Start with an OFDD and change the polarity of variable Xl. Choose the best polarity for Xl and go to the next variable (if it exists) and perform the same optimization.
X -< y /\ Y -< x) above. 4. V x, Y E II : x -< y /\ x rt A I :::} Y rt A I. ,(x -< y /\ Y -< x) above. 5. V X E II : X n 8 9 -::j:. 2): y rt 8 9 :::} V x E X n 8 9 : x -< y :::} r(y, X) > 0 :::} Y rt Xo· 6. V X E II : X n AI -::j:. 5): y rt AI :::} V x E X n AI : x -< y :::} r(y, X) > o:::} y rt Xo. 7. In the special case of 9 = (0, ... ,0) and f = (00, ... 8) that x -< y is equivalent to x 355 terms. But it also takes the largest runtime measured in CPU seconds: more than three times longer than the EA with i = IPI/4. If only a quarter of the initial population is optimized by GRE (dotted line) the runtime is shortened tremendously and the result does not become worse. If the whole population is optimized at the beginning of the EA run (dashed line) the quality is decreased and the runtimes are not as good as for REA using i = IPI/4, because the time consuming heuristic GRE is applied too frequently.
355 terms. But it also takes the largest runtime measured in CPU seconds: more than three times longer than the EA with i = IPI/4. If only a quarter of the initial population is optimized by GRE (dotted line) the runtime is shortened tremendously and the result does not become worse. If the whole population is optimized at the beginning of the EA run (dashed line) the quality is decreased and the runtimes are not as good as for REA using i = IPI/4, because the time consuming heuristic GRE is applied too frequently.