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Radhamanjari Samanta * , Soumyendu Raha * and Adil I. Erzin #

Construction Of A Timing-Driven Variation-Aware Global Router With Concurrent Multi-Net Congestion Optimization. Radhamanjari Samanta * , Soumyendu Raha * and Adil I. Erzin # * Supercomputer Education and Research Centre, Indian Institute of Science , Bangalore, India

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Radhamanjari Samanta * , Soumyendu Raha * and Adil I. Erzin #

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  1. Construction Of A Timing-Driven Variation-Aware Global Router With Concurrent Multi-NetCongestion Optimization RadhamanjariSamanta*, SoumyenduRaha* and Adil I. Erzin# * Supercomputer Education and Research Centre, Indian Institute of Science, Bangalore, India # Sobolev Institute of Mathematics, Siberian Branch, Russian Academy of Sciences, Novosibirsk, Russia TAU 2013

  2. Outline • Introduction • Algorithm MAD(Modified Algorithm Dijkstra) • Experimental results on IBM Benchmark • Statistical (Variation Aware) MAD • Deterministic vs Statistical MAD • Conclusion TAU 2013

  3. ALGORITHM MAD • Constructs a set of Steiner trees for each net in global graph, such that • capacities of the edges are not violated (congestion aware). • delays in primary outputs are upper bounded by the given bounds (timing driven). • Input of algorithm: • Logical network as a set of nets and primary inputs with Arrival Time(AT)s and primary outputs with Required Time(RT)s; • Number of layers; • Specific resistance and capacitance and maximum number of channels Qij (capacity of corresponding global edge) in each layer; • Resistances and capacitances of vias TAU 2013

  4. Steps of Algorithm MAD TAU 2013

  5. An Example execution of MAD TAU 2013

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  17. Congestion-aware tree selection for each net • IMAD(Iterative MAD) is used to build a set of timing-driven Steiner trees for each net. • For each net, a tree is chosen using a gradient algorithm. • The tree is chosen s.t. the minimum residual (current) capacity of global edges is maximum. • This is a concurrent approach considering all the trees of all the nets simultaneously. TAU 2013

  18. Max Overflow with and without Gradient TAU 2013

  19. Total Overflow with and without Gradient TAU 2013

  20. Variation Aware MAD • Process variation becomes prominent in the nano regime. • As a result, delay is no more deterministic. • Derive equivalent statistical MAD by considering process dependent parameters (resistance, capacitance) as Gaussian random variables. • Random variable Mean = deterministic value and standard deviation= 7% of their respective mean. • Mean of 1000 Deterministic Monte Carlo simulations(varied randomly in the range of μ±3σis calculated. • Run the statistical router only once. • Means(Deterministic and statistical) are compared. TAU 2013

  21. Steps of Variation aware MAD • At each step, calculate the minimum distribution of two edges(among all candidate edges). • Find the K-L divergence of minimum distribution from both the distributions. • Choose the edge which has less divergence from min distribution. • In this way, Find the min-delay edge to be added to the tree. • Continue until all sinks are added to the tree. TAU 2013

  22. Exact Distribution of Minimum of two Gaussian R.V. Let X1(μ1, σ12), X2(μ2, σ22) denote two Gaussian random variables. If the distribution of X1 and X2 are non-overlapping, 3σ pruning condition is set. If μ1 + 3σ1 < μ2 − 3σ2 => μ1 − μ2 < −3(σ1 + σ2) => |μ1 − μ2| > 3(σ1 + σ2) then, X1 will be the minimum. TAU 2013

  23. When the distribution of X1 and X2 are overlapping, X = min(X1,X2) will be a different distribution. TAU 2013

  24. Kullback-Leibler Divergence • Finds the nonsymmetric measure of the difference between two probability distributions P and Q. • If P and Q are given probability distributions of a continuous random variable and the densities of P and Q are p and q respectively then, K-L divergence of Q from P is • Symmetrised divergence : TAU 2013

  25. Kullback-Leibler Divergence TAU 2013

  26. Deterministic Monte Carlo Vs Statistical MAD(wl & delay) TAU 2013

  27. Deterministic Monte Carlo Vs Statistical MAD(ovfl & runtime) TAU 2013

  28. Conclusion • Proposed a timing-driven congestion-aware and variation- aware Global Router. • Our router has accurate and fast solution on ibm benchmarks. • Monte Carlo Simulation takes much longer time compared to the time taken by our statistical router. • Statistical Router is more efficient to use than so many Deterministic Monte Carlo Simulations to predict results with process variation. TAU 2013

  29. THANK YOU Contact: samanta@ssl.serc.iisc.in TAU 2013

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