17082553
HyPE: hybrid power estimation for IP-based systems-on-chip
In this paper, we present a novel power estimation scheme for programmable systems consisting of predesigned datapath and memory components. The proposed hybrid methodology yields highly accurate estimates within short runtimes by combining high-level simulation with analytical macromodeling of circuit characteristics. The kernel of our methodology is a simulation-free power estimation scheme for memoryless datapaths comprising several IP blocks connected in fixed topologies. The outer shell of our hybrid scheme is a functional simulation, which is performed only on the interfaces between memoryless components and memory blocks. This simulation accurately captures the control signals that affect the flow of data and, consequently, the utilization and power dissipation of hardware. Experimental results validate the accuracy and efficiency of our methodology. We applied our static power estimation kernel to signal processing and data encryption datapaths. For designs of up to 576 IP blocks, the average error of our power estimates is 7.3% in comparison with switch-level simulation results. We implemented our hybrid scheme into a power estimation tool, called HYPE, and used it to explore various architectural alternatives in the design of a 256-state Viterbi decoder and a Rijndael encryptor. For designs with about 1 million transistors, our estimator terminates within seconds. Compared with commercial state-of-the-art gate-level power estimators, our proposed methodology is up to 1000 times faster with 5.4% deviation on average.
26613748
A Markov chain sequence generator for power macromodeling
In this paper, we present a novel sequence generator based on a Markov chain model. Specifically, we formulate the problem of generating a sequence of vectors with given average input probability p, average transition density d, and spatial correlation s as a transition matrix computation problem, in which the matrix elements are subject to constraints derived from the specified statistics. We also give a practical heuristic that computes such a matrix and generates a sequence of l n-bit vectors in O(nl + n/sup 2/) time. Derived from a strongly mixing Markov chain, our generator yields binary vector sequences with accurate statistics, high uniformity, and high randomness. Experimental results show that our sequence generator can cover more than 99% of the parameter space. Sequences of 2,000 48-bit vectors are generated in less than 0.05 seconds, with average deviations of the signal statistics p, d, and s equal to 1.6%, 1.8%, and 2.8%, respectively. Our generator enables the detailed study of power macromodeling. Using our tool and the ISCAS-85 benchmark circuits, we have assessed the sensitivity of power dissipation to the three input statistics p, d, and s. Our investigation reveals that power is most sensitive to transition density, while only occasionally exhibiting high sensitivity to signal probability and spatial correlation. Our experiments also show that input signal imbalance can cause estimation errors as high as 100% in extreme cases, although errors are usually within 25%.
5855159
Skew spreading for peak current reduction
This paper presents a circuit optimization technique called skewspreading. Given an edge-triggered sequential circuit, skew spreadingderives the required clock arrival times for all registers so that theskews are distributed evenly in a preselected time window without changing the operating frequency of the circuit. Skew spreading is ideal for peak current reduction, since it distributes clock activities and the ensuing signal activities widely in time. We have developed a skew spreading algorithm and applied it to a suite of benchmark circuits. Simulation results demonstrate that the variance of the resulting skew from the uniform distribution can be reduced to 4% on the average. In comparison to other gate-level peak current reduction techniques, our scheme achieves an average improvement of 17% with a speedup of up to 13.9 times.
13241066
Retiming and clock scheduling for digital circuit optimization
This paper investigates the application of simultaneous retiming and clock scheduling for optimizing synchronous circuits under setup and hold constraints. Two optimization problems are explored: (1) clock period minimization and (2) tolerance maximization to clock-signal delay variations. Exact mixed-integer linear programming formulations and efficient heuristics are given for both problems. When both long and short paths are considered, circuits optimized by the combined application of retiming and clock scheduling can achieve shorter clock periods or demonstrate greater tolerance to clock-signal delay variations than circuits optimized by retiming or clock scheduling. Experiments with benchmark circuits demonstrate the effectiveness of the combined optimization. In comparison with the best result obtained by either of the two optimizations, the joint application of retiming and clock scheduling increased operating speeds by more than 8% on the average. It also increased tolerance to clock delay variations by an average of 12% over a broad range of target clock frequencies. Larger relative improvements were achieved for shorter clock periods, thus suggesting that simultaneous retiming and clock scheduling can play an important role in high-speed design.
1752593
RITC: repeater insertion with timing target compensation
This paper investigates the problem of repeater insertion for global interconnects under given timing constraints. A novel technique is proposed to aggressively reduce the positive timing slack for maximal repeater reduction while maintaining timing closure. Our scheme is both fast and effective due to the judicious combination of accurate SPICE simulations and the simple Elmore delay model. In comparison with other repeater insertion solvers, our scheme achieves up to 41% reduction in total repeater width in comparable runtimes.
10662727
Power macromodeling of global interconnects considering practical repeater insertion
In this paper, we present a simple yet highly effective power macromodeling technique for global interconnects that considers optimal repeater insertion. Specifically,our model estimates the interconnect power dissipation from the interconnect length, the timing budget, the repeater location flexibility, and the signal activity using an analytical function that is derived for a given repeater library and fabrication technology. In experiments with different standard cell libraries, the average error of our technique is less than 2%.
17821282
Clustering with Niching Genetic K-means Algorithm
GA-based clustering algorithms often employ either simple GA, steady state GA or their variants and fail to consistently and efficiently identify high quality solutions (best known optima) of given clustering problems, which involve large data sets with many local optima. To circumvent this problem, we propose Niching Genetic K-means Algorithm (NGKA) that is based on modified deterministic crowding and embeds the computationally attractive k-means. Our experiments show that NGKA can consistently and efficiently identify high quality solutions. Experiments use both simulated and real data with varying size and varying number of local optima. The significance of NGKA is also shown on the experimental data sets by comparing through simulations with Genetically Guided Algorithm (GGA) and Genetic K-means Algorithm (GKA).
9790968
Fuzzy approximate disturbance decoupling of MIMO nonlinear systems by backstepping and application to chemical processes
Fuzzy approximate disturbance decoupling concept is introduced for a class of multiple-input-multiple-output (MIMO) nonlinear systems with completely unknown nonlinearities. Based on backstepping technique, a fuzzy almost disturbance decoupling control scheme is proposed. The fuzzy controllers guarantee internal uniform ultimate boundedness of the closed-loop adaptive systems and render a bounded approximate L/sub 2/ gain from the disturbance input to the output. The developed design scheme is applied to control a two continuous stirred tank reactor process. The simulation results illustrate the effectiveness of the method proposed in this paper.
16058917
Learning Dynamic Bayesian Networks from Multivariate Time Series with Changing Dependencies
Many examples exist of multivariate time series where dependencies between variables change over time. If these changing dependencies are not taken into account, any model that is learnt from the data will average over the different dependency structures. Paradigms that try to explain underlying processes and observed events in multivariate time series must explicitly model these changes in order to allow non-experts to analyse and understand such data. In this paper we have developed a method for generating explanations in multivariate time series that takes into account changing dependency structure. We make use of a dynamic Bayesian network model with hidden nodes. We introduce a representation and search technique for learning such models from data and test it on synthetic time series and real-world data from an oil refinery, both of which contain changing underlying structure. We compare our method to an existing EM-based method for learning structure. Results are very promising for our method and we include sample explanations, generated from models learnt from the refinery dataset.
32095656
Analyzing Outliers Cautiously
Outliers are difficult to handle because some of them can be measurement errors, while others may represent phenomena of interest, something "significant" from the viewpoint of the application domain. Statistical and computational methods have been proposed to detect outliers, but further analysis of outliers requires much relevant domain knowledge. In our previous work (1994), we suggested a knowledge-based method for distinguishing between the measurement errors and phenomena of interest by modeling "real measurements" - how measurements should be distributed in an application domain. In this paper, we make this distinction by modeling measurement errors instead. This is a cautious approach to outlier analysis, which has been successfully applied to a medical problem and may find interesting applications in other domains such as science, engineering, finance, and economics.
16232707
A weighted sum validity function for clustering with a hybrid niching genetic algorithm
Clustering is inherently a difficult problem, both with respect to the construction of adequate objective functions as well as to the optimization of the objective functions. In this paper, we suggest an objective function called the Weighted Sum Validity Function (WSVF), which is a weighted sum of the several normalized cluster validity functions. Further, we propose a Hybrid Niching Genetic Algorithm (HNGA), which can be used for the optimization of the WSVF to automatically evolve the proper number of clusters as well as appropriate partitioning of the data set. Within the HNGA, a niching method is developed to preserve both the diversity of the population with respect to the number of clusters encoded in the individuals and the diversity of the subpopulation with the same number of clusters during the search. In addition, we hybridize the niching method with the k-means algorithm. In the experiments, we show the effectiveness of both the HNGA and the WSVF. In comparison with other related genetic clustering algorithms, the HNGA can consistently and efficiently converge to the best known optimum corresponding to the given data in concurrence with the convergence result. The WSVF is found generally able to improve the confidence of clustering solutions and achieve more accurate and robust results.