High-level synthesis is a novel method to generate a RT-level hardware description automatically from a high-level language such as C, and is used at recent digital circuit design. Floating-point to fixed-point conversion with bit-length optimization is one of the key issues for the area and speed optimization in high-level synthesis. However, the conversion task is a rather tedious work for designers. This paper introduces automatic bit-length optimization method on floating-point to fixed-point conversion for high-level synthesis. The method estimates computational errors statistically, and formalizes an optimization problem as a non-linear problem. The application of NLP technique improves the balancing between computational accuracy and total hardware cost. Various constraints such as unit sharing, maximum bit-length of function units can be modeled easily, too. Experimental result shows that our method is fast compared with typical one, and reduces the hardware area.

In this paper, we present an efficient method to budget on-chip decoupling capacitors (decaps) to optimize power delivery networks in an area efficient way. Our algorithm is based on an efficient gradient-based non-linear programming method for searching the solution. Our contributions are an efficient gradient computation method (time-domain merged adjoint network method) and a novel equivalent circuit modeling technique to speed up the optimization process. Experimental results demonstrate that the algorithm is capable of efficiently optimizing very large scale P/G networks.

In this paper, we study the link capacity assignment problem in packet-switched networks (CA problem) focusing on the case where link cost function is a piecewise linear concave function. This type of cost function arises in many communication network design problems such as those arising from developments in communication transmission technologies. It is already known that the method of link set assignment is applicable for solving the CA problem with piecewise linear convex cost function. That is, each link in the network is assigned to one of a group of specific sets, and checked for link set contradiction. By extending the method of link set assignment to the case of piecewise linear concave cost function, an important characteristic of the optimal solution of the CA problem is derived. Based on this characteristic, the non-differentiable link cost function can be treated as a differentiable function, and a heuristic algorithm derived from the Lagrange multiplier method is then proposed. Although it is difficult to determine the global optimum of the CA problem due to its non-convexity, it is shown by numerical results that the solution obtained from the proposed algorithm is very close to the global optimum. Moreover, the computation time is linearly dependent on the number of links in the problem. These performances show that the proposed algorithm is very efficient in solving the CA problem, even in the case of large-scale networks.

When communication network planning-design is performed, especially in a short-term case, it is important to utilize existing facilities in the construction of the new network. In this paper, link capacity assignment problem (CA problem) for packet-switched networks is investigated with the consideration of the existing network. To deal with this, per-unit cost of existing link capacity is thought to be less than that of newly installed capacity and a link cost function is modeled by a non-linear, non-differentiable one which is composed of two portions of capacity cost. After formulating the CA problem, two optimum algorithms derived from Lagrange multiplier method are presented and a modified algorithm is used for solving the CA problem in order to reduce the computation time. Some numerical results show that according to the values of link traffic flows, there will be links whose capacities must be set equally to the existing values. Moreover, when link cost difference is introduced in the CA problem, the number of links that the capacities of which have to be changed from existing values is less than that of linear cost function case, i.e., the case without consideration of the cost difference in link capacity.