1644440
Design of a high-throughput low-power IS95 Viterbi decoder
The design of high-throughput large-state Viterbi decoders relies on the use of multiple arithmetic units. The global communication channels among these parallel processors often consist of long interconnect wires, resulting in large area and high power consumption. In this paper, we propose a data-transfer oriented design methodology to implement a low-power 256-state rate-1/3 IS95 Viterbi decoder. Our architectural level scheme uses operation partitioning, packing, and scheduling to analyze and optimize interconnect effects in early design stages. In comparison with other published Viterbi decoders, our approach reduces the global data transfers by up to 75% and decreases the amount of global buses by up to 48%, while enabling the use of deeply pipelined datapaths with no data forwarding. In the RTL implementation of the individual processors, we apply precomputation in conjunction with saturation arithmetic to further reduce power dissipation with provably no coding performance degradation. Designed using a 0.25 μ standard cell library, our decoder achieves a throughput of 20 Mbps in simulation and dissipates only 450 mW.
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%.
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%.
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.
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.
17545097
Design of Rotary Clock Based Circuits
Rotary clock technique has been shown to reduce the power dissipation of clock distribution by up to 80%. However, to our knowledge, no practical digital synchronous circuit has been designed that utilizes the rotary technique due to the multi-phase nature of rotary clock signals and the strong coupling between a rotary clock network and the circuit it drives. In this paper, we make the first attempt to design rotary based circuit by proposing a unified clock and circuit design methodology. Given a sequential circuit and a clock frequency, our scheme derives a rotary clock network and a functionally equivalent circuit so that they can be integrated to operate reliably at the target frequency. We have applied our scheme to design several rotary clock based DSP modules. Simulation results show that all designs operate correctly with no timing violation. Compared with designs using conventional clock trees, our designs achieve an average clock power saving of more than 70%.
21789348
Evolutionary algorithms for grouping high dimensional Email data
Grouping problems arise in many industrial and medical applications; examples include bin packing, workshop layout design, and graph colouring. This type of problem has been successfully handled using Grouping Genetic Algorithms. However in problems where there are perhaps thousands of objects to be grouped, we have found that Genetic Algorithm approaches can run into problems. This paper continues our research into a method we have developed for decomposing a large number of objects into mutually exclusive subsets where within-group dependencies are high and between-group dependencies are low. The method uses an Evolutionary Algorithm approach but where the whole population is a solution to the grouping problem rather than considering many candidate solutions. This reduces the resource overheads during computer implementation and the results are promising when compared with standard statistical methods and a Hill Climbing algorithm, all applied to email log file data.
12314143
Combs, needles, haystacks: balancing push and pull for discovery in large-scale sensor networks
In this paper we investigate efficient strategies for supporting on-demand information dissemination and gathering in large-scale vwireless sensor networks. In particular, we propose a "comb-needle" discovery support model resembling an ancient method: use a comb to help find a needle in sands or a haystack. The model combines push and pull for information dissemination and gathering. The push component features data duplication in a linear neighborhood of each node. The pull component features a dynamic formation of an on-demand routing structure resembling a comb. The comb-needle model enables us to investigate the cost of a spectrum of push and pull combinations for supporting discovery and query in large scale sensor networks. Our result shows that the optimal routing structure depends on the frequency of query occurrence and the spatial-temporal frequency of related events in the network. The benefit of balancing push and pull for discovery in large scale geometric networks are demonstrated. We also raise the issue of query coverage in unreliable networks and investigate how redundancy can improve the coverage via both theoretical analysis and simulation. Last, we study adaptive strategies for the case where the frequencies of query and events are unknown a priori and time-varying.
2180147
An experimental evaluation of a loop versus a reference design for two-channel microarrays
MOTIVATION
Despite theoretical arguments that so-called 'loop designs' for two-channel DNA microarray experiments are more efficient, biologists continue to use 'reference designs'. We describe two sets of microarray experiments with RNA from two different biological systems (TPA-stimulated mammalian cells and Streptomyces coelicolor). In each case, both a loop and a reference design were used with the same RNA preparations with the aim of studying their relative efficiency.


RESULTS
The results of these experiments show that (1) the loop design attains a much higher precision than the reference design, (2) multiplicative spot effects are a large source of variability, and if they are not accounted for in the mathematical model, for example, by taking log-ratios or including spot effects, then the model will perform poorly. The first result is reinforced by a simulation study. Practical recommendations are given on how simple loop designs can be extended to more realistic experimental designs and how standard statistical methods allow the experimentalist to use and interpret the results from loop designs in practice.


AVAILABILITY
The data and R code are available at http://exgen.ma.umist.ac.uk


CONTACT
veronica.vinciotti@brunel.ac.uk.
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.
31090021
A genetic k-medoids clustering algorithm
We propose a hybrid genetic algorithm for k-medoids clustering. A novel heuristic operator is designed and integrated with the genetic algorithm to fine-tune the search. Further, variable length individuals that encode different number of medoids (clusters) are used for evolution with a modified Davies-Bouldin index as a measure of the fitness of the corresponding partitionings. As a result the proposed algorithm can efficiently evolve appropriate partitionings while making no a priori assumption about the number of clusters present in the datasets. In the experiments, we show the effectiveness of the proposed algorithm and compare it with other related clustering methods.