Detailed Placement and Global Routing Co-optimization with Complex Constraints

With several divided stages, placement and routing are the most critical and challenging steps in VLSI physical design. To ensure that physical implementation problems can be manageable and converged in a reasonable runtime, placement/routing problems are usually further split into several sub-problems, which may cause conservative margin reservation and mis-correlation. Therefore, it is desirable to design an algorithm that can accurately and efficiently consider placement and routing simultaneously. In this paper, we propose a detailed placement and global routing co-optimization algorithm while considering complex routing constraints to avoid conservative margin reservation and mis-correlation in placement/routing stages. Firstly, we present a rapidly preprocessing technology based on R-tree to improve the initial routing results. After that, a BFS-based approximate optimal addressing algorithm in 3D is designed to find a proper destination for cell movement. We propose an optimal region selection algorithm based on the partial routing solution to jump out of the local optimal solution. Further, a fast partial net rip-up and rerouted algorithm is used in the process of cell movement. Finally, we adopt an efficient refinement technique to reduce the routing length further. Compared with the top 3 winners according to the 2020 ICCAD CAD contest benchmarks, the experimental results show that our algorithm achieves the best routing length reduction for all cases with a shorter runtime. On average, our algorithm can improve 0.7%, 1.5%, and 1.7% for the first, second, and third place, respectively. In addition, we can still obtain the best results after relaxing the maximum cell movement constraint, which further illustrates the effectiveness of our algorithm.

Investigating optimal region for thermal and electrical properties of epoxy nanocomposites under high frequencies and temperatures

This research investigates the optimal region to achieve balanced thermal and electrical insulation properties of epoxy (EP) under high frequency (HF) and high temperature (HT) via integration of surface-modified hexagonal boron nitride (h-BN) nanoparticles. The effects of nanoparticle content and high temperature on various electrical (DC, AC, and high frequency) and thermal properties of EP are investigated. It is found that the nano h-BN addition enhances thermal performance and weakens electrical insulation properties. On the other side, under HF and HT stress, the presence of h-BN nanoparticles significantly improves the electrical performance of BN/EP nanocomposites. The EP has superior insulation properties at low temperature and low frequency, whereas the BN/EP nanocomposites exhibit better insulation performance than EP under HF and HT. The factors such as homogeneous nanoparticle dispersion in EP, enhanced thermal conductivity, nanoparticle surface modification, weight percent of nanoparticles, the mismatch between the relative permittivity of EP and nano h-BN, and the presence of voids in nanocomposites play the crucial role. The optimal nanoparticle content and homogenous dispersion can produce suitable EP composites for the high frequency and high temperature environment, particularly solid-state transformer applications.

Optimal Region Gradated Fusion Based Automatic Image Stitching for Industrial Systems

In the process of equipment production in large manufacturing, the continuity of production is becoming more significant. Timely detection of equipment operation faults can ensure production continuity and greatly reduce loss. In this study, the purpose is to use multi-equipment and image stitching algorithm to obtain the complete image of a large-scale production line. An improved image stitching method based on image fusion is proposed in this paper, which mainly solves the technical problems of stitching seams, unnatural effects, and distortion after image transformation in the existing stitching technique. In the image stitching algorithm, the improved fusion algorithm based on the optimal seam and gradated in and out fusion algorithm is used to realize image fusion, including the use of dynamic programming to find the optimal seam and limit the range of fusion based on the optimal seam found. Finally, the gradated in and out fusion algorithm is used to perform fusion calculation within the limited fusion range to complete image stitching. In the end, through the comparison of different dimensional image fusion indicators with the effect of the existing fusion algorithm, the experimental results show that the method in this paper solves the problem of unnatural image stitching effect, enhances the image stitching result, and has the great fusion effect. Therefore, the panorama processed by the image stitching algorithm proposed in this paper can be efficiently processed through the industrial detection module.

Error rate control for classification rules in multiclass mixture models

In the context of finite mixture models one considers the problem of classifying as many observations as possible in the classes of interest while controlling the classification error rate in these same classes. Similar to what is done in the framework of statistical test theory, different type I and type II-like classification error rates can be defined, along with their associated optimal rules, where optimality is defined as minimizing type II error rate while controlling type I error rate at some nominal level. It is first shown that finding an optimal classification rule boils down to searching an optimal region in the observation space where to apply the classical Maximum A Posteriori (MAP) rule. Depending on the misclassification rate to be controlled, the shape of the optimal region is provided, along with a heuristic to compute the optimal classification rule in practice. In particular, a multiclass FDR-like optimal rule is defined and compared to the thresholded MAP rules that is used in most applications. It is shown on both simulated and real datasets that the FDR-like optimal rule may be significantly less conservative than the thresholded MAP rule.

A new path planning method based on sparse A* algorithm with map segmentation

Due to the complexity of map modeling, the massive computation and high redundancy of the traditional A* algorithm will greatly reduce the efficiency of pathfinding, resulting in huge performance consumption. Meanwhile, limited by neighborhood search strategy in grid map, the traditional A* algorithm is actually unable to achieve the optimal path in the global sense. To solve these problems, this paper proposes an improved A* algorithm based on graph preprocessing. First, the free space on the map was decomposed into several polygon regions using the improved convex decomposition method based on Maklink. Then, each region was coded into feature nodes according to A* algorithm. Finally, an optimal region passage was found based on the principle of A* algorithm, in which the global optimal path solution was obtained. Compared with the traditional A* algorithm and other classical path planning algorithms, the proposed algorithm has significant advantages in planning speed, path cost, stability, and completeness.

From an Optimal Point to an Optimal Region: A Novel Methodology for Optimization of Multimodal Constrained Problems and a Novel Constrained Sliding Particle Swarm Optimization Strategy

The present work proposes a novel methodology for an optimization procedure extending the optimal point to an optimal area based on an uncertainty map of deterministic optimization. To do so, this work proposes the deductions of a likelihood-based test to draw confidence regions of population-based optimizations. A novel Constrained Sliding Particle Swarm Optimization algorithm is also proposed that can cope with the optimization procedures characterized by multi-local minima. There are two open issues in the optimization literature, uncertainty analysis of the deterministic optimization and application of meta-heuristic algorithms to solve multi-local minima problems. The proposed methodology was evaluated in a series of five benchmark tests. The results demonstrated that the methodology is able to identify all the local minima and the global one, if any. Moreover, it was able to draw the confidence regions of all minima found by the optimization algorithm, hence, extending the optimal point to an optimal region. Moreover, providing the set of decision variables that can give an optimal value, with statistical confidence. Finally, the methodology is evaluated to address a case study from chemical engineering; the optimization of a complex multifunctional process where separation and reaction are processed simultaneously, a true moving bed reactor. The method was able to efficiently identify the two possible optimal operating regions of this process. Therefore, proving the practical application of this methodology.