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History-based VLSI Legalization using Network Flow


									History-based VLSI Legalization
           using Network Flow
       Minsik Cho, Haoxing Ren, Hua Xiang, Ruchir Puri
   Introduction & Contribution
   Problem Formulation
   Algorithm
       Network Flow Formulation
       Flow Realization
       Region Placement
       History Learning
   Experimental Results
   Conclusion & Future Work
   The high density of design in a chip affects the entire physical
   Take care of placement can reduce the complexity of the
    following stages:
       buffering, gate sizing, routing, etc.
   The main goal of placement
       Locate all the objects without overlap
       Satisfying each kinds of design objectives.
   Legalization is an important step between global and detail
    placement to remove all the overlaps with minimum
    perturbation or impaction.
   A novel gate-centric MCMF formulation
       Optimize the deviation for each gate better
       History scheme can be integrated smoothly

   Incorporate a history-based technique into a new
    network flow formulation

   Propose efficient techniques to realize a flow into gate
    movements based on a Subset-sum problem
Problem Formulations
   A rectangle chip is partitioned into equal-sized circuit
    rows, and each row is further divided into block-free
   The Manhattan distance between these two positions is
    defined as deviation
 Problem Formulations (cont)

                               Maximum deviation
Average weighted sum
Further Issues
   Base on network flow, how to set the sources and sinks
    by this formula?
       Set all the gates in one of overflow regions as sources, other
        regions as sinks.
   How to solve that general network flow cannot model
    discrete sizes of gates?
       Using unbounded or maximum width size of gate as limit of
        flow and do the Flow Realization.
   How to calculate the deviation of the y-value?
       Using the center Yr+Wr/2 to evaluate the deviation, and do
        the Region Placement.
Algorithm Flow
Example: Compare Flow and Greedy
Step 1: Network Flow
                                  Might move partially

        Times of history failed
Step 2: Flow Realization
   There is a partial flow from A to the empty space
   Solve Subset-sum problem(NP class)
       Partition into two set and fit the
        regions with cheapest solution.
       We set the size solution T < λ
       Control the bounded flow and λ
        can reduce run time.
       It returns Failure if the problem
        is unsolvable.
Step 3: Region Placement
   Find y-value of all gates in a region for minimum deviation
    is NP-Hard.
       Order the gates according to the center location (xi, yi+wi/2)
        rather than (xi, yi), and it provides the less deviation according
        to experience.
       In case of overflow region, we temporarily scale down wi with
        Wr/Or, just make placement fesiable.
       Solve this problem by single row placement refer to [3,4,7, 11].
Step 4: History Learning
   To avoid unrealizable flow, increasing the history factor
    h[wi][r][p] for the cost expensive enough.

                      Success flow on
                      the 5th iteration
Post-Optimization & Speedup
    When we get a legal solution, it is possible that some gates
     have large deviation.
        Greedily move gates toward their initial position.
        Using Flow Realization with zero flow

    The complexity of network flow is:

        Boundle tightly coupled gates if the total width less than maximum
         width in library, it can reduce |I| effectively.
        In most case, a gate migrates to the nearby regions, we can
         insert the edges by user defined. (reduce |E|)
        Using hierarchical approach to reduce |R|.
Experiment Results
   Environment
       Implement in C++
       2.4GHz Linux machine with 4G RAM
   Competitor
       NTUPlace3-LE, FastPlace3, Dragon2006, BonnL
   Benchmark
       45nm with mixed-sized blockages and fixed gates
       From the industrial global placer
   Ignore
       Wire length optimization
Experiment Results (cont)
   Failure rate comparison
Experiment Result (cont)
   Compare QoR with NTUPlace
Conclusion & Future Work
   Using History-based MCMF to solve the general
    problems cannot solve by normal MCMF.
   Simultaneously legalization often get better QoR.

   Using the history-based technique
       The assignment problem with the in-flow of a souce more than
       The cost of SA-based problem.
   By solving the Subset-sum problem
       We can solve network flow out of bound to get more
        optimization chances.

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