Modeling of a hardware VLSI placement system: Accelerating the Simulated Annealing algorithm

An essential step in the automation of electronic design is the placement of the physical components on the target semiconductor die. The placement step presents the opportunity to reduce costs in terms of wire length and performance degradation; however it is compute intensive and is NP-complete in terms of obtaining an optimal solution. As designs have grown in complexity and gate count, obtaining an optimal solution is not feasible due to time to market constraints or sheer compute effort required. Heuristic algorithms allow for efficient but sub-optimal designs to be produced with a reduction in processing time. A widely used algorithm is Simulated Annealing (SA). The goal of this work was to develop a model that would enable an analysis into the feasibility of developing a hardware accelerated placement system which uses SA at its core. The SA heuristic was analyzed for possible improvements in efficiency with focus given to targeting the system for hardware. A solution implementing parallel computing with specialized hardware configurations inside a field programmable gate array (FPGA) was investigated as having the possibility to improve the efficiency of the SA-based algorithm. All supporting subsystems were also described for a hardware accelerated model. A large speedup was analytically shown from both accelerating the critical path of the SA algorithm as well as novel methods of improving SA\u27s efficiency. As data throughput requirements were not included in this work, the results presented may be optimistic for an overall system speedup. However, the results clearly show that future work is warranted in studying the concept of a hardware accelerated placement system

Discovering the hidden structure of financial markets through bayesian modelling

Understanding what is driving the price of a financial asset is a question that is currently mostly unanswered. In this work we go beyond the classic one step ahead prediction and instead construct models that create new information on the behaviour of these time series. Our aim is to get a better understanding of the hidden structures that drive the moves of each financial time series and thus the market as a whole. We propose a tool to decompose multiple time series into economically-meaningful variables to explain the endogenous and exogenous factors driving their underlying variability. The methodology we introduce goes beyond the direct model forecast. Indeed, since our model continuously adapts its variables and coefficients, we can study the time series of coefficients and selected variables. We also present a model to construct the causal graph of relations between these time series and include them in the exogenous factors. Hence, we obtain a model able to explain what is driving the move of both each specific time series and the market as a whole. In addition, the obtained graph of the time series provides new information on the underlying risk structure of this environment. With this deeper understanding of the hidden structure we propose novel ways to detect and forecast risks in the market. We investigate our results with inferences up to one month into the future using stocks, FX futures and ETF futures, demonstrating its superior performance according to accuracy of large moves, longer-term prediction and consistency over time. We also go in more details on the economic interpretation of the new variables and discuss the created graph structure of the market.Open Acces

ATP and its N6-substituted analogues: parameterization, molecular dynamics simulation and conformational analysis

In this work we used a combination of classical molecular dynamics and simulated annealing techniques to shed more light on the conformational flexibility of 12 adenosine triphosphate (ATP) analogues in a water environment. We present simulations in AMBER force field for ATP and 12 published analogues [Shah et al. (1997) Proc Natl Acad Sci USA 94: 3565–3570]. The calculations were carried out using the generalized Born (GB) solvation model in the presence of the cation Mg2+. The ion was placed at a close distance (2 Å) from the charged oxygen atoms of the beta and gamma phosphate groups of the −3 negatively charged ATP analogue molecules. Analysis of the results revealed the distribution of inter-proton distances H8–H1′ and H8–H2′ versus the torsion angle ψ (C4–N9-C1′–O4′) for all conformations of ATP analogues. There are two gaps in the distribution of torsion angle ψ values: the first is between −30 and 30 degrees and is described by cis-conformation; and the second is between 90 and 175 degrees, which mostly covers a region of anti conformation. Our results compare favorably with results obtained in experimental assays [Jiang and Mao (2002) Polyhedron 21:435–438]

Exploring Asynchronous MMC based Parallel SA Schemes for Multiobjective Cell Placement on a Cluster-of-Workstations

Simulated Annealing (SA) is a popular iterative heuristic used to solve a wide variety of combinatorial optimization problems. However, depending on the size of the problem, it may have large run-time requirements. One practical approach to speed up its execution is to parallelize it. In this paper, several parallel SA schemes based on the Asynchronous Multiple-Markov Chain model are explored. We investigate the speedup and solution quality characteristics of each scheme when implemented on an inexpensive cluster of workstations for solving a multi-objective cell placement problem. This problem requires the optimization of conicting objectives (interconnect wire-length, power dissipation, and timing performance), and Fuzzy logic is used to integrate the costs of these objectives. Our goal is to develop several AMMC based parallel SA schemes and explore their suitability for different objectives: achieving near linear speedups while still meeting solution quality targets, and obtaining higher quality solutions in the least possible duration

Exploring Asynchronous MMC based Parallel SA Schemes for Multiobjective Cell Placement on a Cluster-of-Workstations

Simulated Annealing (SA) is a popular iterative heuristic used to solve a wide variety of combinatorial optimization problems. However, depending on the size of the problem, it may have large run-time requirements. One practical approach to speed up its execution is to parallelize it. In this paper, several parallel SA schemes based on the Asynchronous Multiple-Markov Chain model are explored. We investigate the speedup and solution quality characteristics of each scheme when implemented on an inexpensive cluster of workstations for solving a multi-objective cell placement problem. This problem requires the optimization of conicting objectives (interconnect wire-length, power dissipation, and timing performance), and Fuzzy logic is used to integrate the costs of these objectives. Our goal is to develop several AMMC based parallel SA schemes and explore their suitability for different objectives: achieving near linear speedups while still meeting solution quality targets, and obtaining higher quality solutions in the least possible duration