In this paper, we present a new voltage IR drop analysis approach for large on-chip power delivery networks. The new approach is based on recently proposed sampling based reduction technique to reduce the circuit matrices before the simulation. Due to the disruptive nature of tap current waveforms in typical industry power grid networks, input current sources typically has wide frequency power spectrum. To avoid the excessively sampling, the new approach introduces an error check mechanism and on-the-fly error reduction scheme during the simulation of the reduced circuits to improve the accuracy of estimating the the large IR drops. The proposed method presents a new way to combine model order reduction and simulation to achieve the overall efficiency of simulation. The new method can also easily trade errors for speed for different applications. Experimental results show the proposed IR drop analysis method can significantly reduce the errors of the existing ETBR method at the similar computing cost, while it can have 10X and more speedup over the the commercial power grid simulator in UltraSim with about 1-2% errors on a number of real industry benchmark circuits.

In this paper, we present a novel statistical full-chip leakage power analysis method. The new method can provide a general framework to derive the full-chip leakage current or power in a closed form in terms of the variational parameters, such as the channel length, the gate oxide thickness, etc. It can accommodate various spatial correlations. The new method employs the orthogonal polynomials to represent the variational gate leakages in a closed form first, which is generated by a fast multi-dimensional Gaussian quadrature method. The total leakage currents then are computed by simply summing up the resulting orthogonal polynomials (their coefficients). Unlike many existing approaches, no grid-based partitioning and approximation are required. Instead, the spatial correlations are naturally handled by orthogonal decompositions. The proposed method is very efficient and it becomes linear in the presence of strong spatial correlations. Experimental results show that the proposed method is about 10× faster than the recently proposed method [4] with constant better accuracy.

This paper proposes a novel stochastic method for analyzing the voltage drop variations of on-chip power grid networks, considering lognormal leakage current variations. The new method, calledStoEKS, applies Hermite polynomial chaos to represent the random variables in both power grid networks and input leakage currents. However, different from the existing orthogonal polynomial-based stochastic simulation method, extended Krylov subspace (EKS) method is employed to compute variational responses from the augmented matrices consisting of the coefficients of Hermite polynomials. Our contribution lies in the acceleration of the spectral stochastic method using the EKS method to fast solve the variational circuit equations for the first time. By using the reduction technique, the new method partially mitigates increased circuit-size problem associated with the augmented matrices from the Galerkin-based spectral stochastic method. Experimental results show that the proposed method is about two-order magnitude faster than the existing Hermite PC-based simulation method and many order of magnitudes faster than Monte Carlo methods with marginal errors. StoEKS is scalable for analyzing much larger circuits than the existing Hermit PC-based methods.

In this paper, we propose a novel stochastic method for analyzing the voltage drop variations of on-chip power grid networks with log-normal leakage current variations. The new-method, called StoEKS, applies Hermite polynomial chaos (PC) to represent the random variables in both power grid networks and input leakage currents. But different from the existing Hermit PC based stochastic simulation method, extended Krylov subspace method (EKS) is employed to compute variational responses using the augmented matrices consisting of the coefficients of Hermite polynomials. Our contribution lies in the combination of the statistical spectrum method with the extended Krylov subspace method to fast solve the variational circuit equations for the first time. Experimental results show that the proposed method is about two-order magnitude faster than the existing Hermite PC based simulation method and more order of magnitudes faster than Monte Carlo methods with marginal errors. StoEKS also can analyze much larger circuits than the exiting Hermit PC based methods.

In this paper, we propose a novel on-chip voltage drop reduction technique for on-chip power delivery networks of VLSI systems in the presence of variational leakage current sources. The new method inserts decoupling capacitors (decaps) into the power grid networks to reduce the voltage fluctuation. The optimization is based on sensitivity-based conjugate gradientmethod and sequence of linear programming approach. Different from existing power grid noise reduction methods, the new approach considers the impacts of inter-die and intra-die variational leakage current sources due to unavoidable process variability during the decap optimization process for the first time. Leakage currents, which although are static in nature typically, can still add to the total voltage drops and dynamic voltage reduction thus must consider the leakage-induced voltage variations. The proposed algorithm exploits the relative constant variations for different decap configurations of power grid circuits to speed up the statistical optimization process. Decaps can be inserted in such a way that the resulting circuits have much higher probability to meet the voltage drop constraints in the presence of leakage current variations. Experimental results demonstrate the effectiveness of the proposed approach and show that the new method has 100X to 1,000X of speedup over the Monte Carlo based statistical decap optimization method.

As the technology scales into 90 nm and below, process-induced variations become more pronounced. In this paper, we propose an efficient stochastic method for analyzing the voltage drop variations of on-chip power grid networks, considering log-normal leakage current variations with spatial correlation. The new analysis is based on the Hermite polynomial chaos (PC) representation of random processes. Different from the existing Hermite PC based method for power grid analysis, which models all the random variations as Gaussian processes without considering spatial correlation. The new method focuses on the impacts of stochastic sub-threshold leakage currents, which are modeled as log-normal distribution random variables, on the power grid voltage variations. To consider the spatial correlation, we apply orthogonal decomposition to map the correlated random variables into independent variables. Our experiment results show that the new method is more accurate than the Gaussian-only Hermite PC method using the Taylor expansion method for analyzing leakage current variations, and two orders of magnitude faster than the Monte Carlo method with small variance errors. We also show that the spatial correlation may lead to large errors if not being considered in the statistical analysis.