Risk-aware Data Replication (RDR), which replicates data at primary sites to nearby safe backup sites, has been proposed to mitigate service disruption in a disaster area even after a widespread disaster that damages a network and a primary site. RDR assigns a safe backup site to a primary site while considering damage risk for both the primary site and the backup candidate site. To minimize the damage risk of all site-pairs the Integer Programing Problem (IPP), which is a mathematical optimization problem, is applied. A challenge for RDR is to choose safe backup sites within a short computation time even for a huge number of sites. As described in this paper, we propose a Discreet method for RDR to surmount this hurdle. The Discreet method first judges the backup sites of a potentially unsafe primary site and avoids assigning a very safe primary site with a very safe backup site. We evaluated the computation time for site-paring and the data availability in the cases of Earthquake and Tsunami using basic disaster simulations. We confirmed that the computation rate of the proposed method is more than 1000 times faster than the existing method when the number of sites is greater than 1000. We also confirmed the data availability of the proposed method; it provides almost equal rates to existing methods of strict optimization. These results mean that the proposed method makes RDR more practical for massively multiple sites.

A novel method to efficiently synthesize hardware from a large behavioral description in behavioral synthesis is proposed. For a program with functions executable in parallel, this proposed method determines a behavioral partitioning which simultaneously minimizes the overall datapath area and the complexity of the controller while maximizing performance of a synthesized circuit by fully exploiting function-level parallelism of a behavioral description. This method is formulated as an integer programming problem. Experimental results demonstrate that this method leads to a shift of the explorable design space so that superior solutions which could not be explored by earlier work are included, showing the effectiveness of our proposed method.

This paper proposes a behavioral level partitioning method for efficient behavioral synthesis from a large sequential program consisting of a set of functions. Our method optimally determines functions to be inlined into the main module and the other functions to be synthesized into sub modules in such a way that the overall datapath is minimized while the complexity of individual modules is lower than a certain level. The partitioning problem is formulated as an integer programming problem. Experimental results show the effectiveness of the proposed method.

Behavioral synthesis, which automatically synthesizes an RTL circuit from a sequential program, is one of promising technologies to improve the design productivity. This paper proposes a function call optimization method in behavioral synthesis from large sequential programs with a number of functions. We formulate the optimization problem using integer linear programming. Our experimental results show the reduction in the circuit area by up to 44.6%, compared with a traditional method.

A new approach is described for the datapath scheduling of behavioral descriptions containing nested conditional branches of arbitrary structures. This paper first investigates such a complex scheduling mechanism, and formulates an optimal scheduling problem as a 0-1 integer programming problem such that given a prescribed number of control steps, the total cost of functional units can be minimized. In this formulation, each constraint is expressed in the form of a Boolean function, which is set equal to 1 or 0 according as the constraint is satisfied or not, respectively, and a satisfiability problem is defined by the product of the Boolean functions. A procedure is then described, which intends to seek an optimal solution by means of a branch-and-bound method on a binary decision diagram representing the satisfiability problem. Experimental results are also shown, which demonstrate that our approach is of more practical use than the existing methods.