Simultaneous block and I/O buffer floorplanning for flip-chip design

The flip-chip package gives the highest chip density of any packaging method to support the pad-limited ASIC design. One of the most important characteristics of flip-chip designs is that the input/output buffers could be placed anywhere inside a chip. In this paper, we first introduce the floorplanning problem for the flip-chip design and formulate it as assigning the positions of input/output buffers and first-stage/last-stage blocks so that the path length between blocks and bump balls as well as the delay skew of the paths are simultaneously minimized. We then present a hierarchical method to solve the problem. We first cluster a block and its corresponding buffers to reduce the problem size. Then, we go into iterations of the alternating and interacting global optimization step and the partitioning step. The global optimization step places blocks based on simulated annealing using the B*-tree representation to minimize a given cost function. The partitioning step dissects the chip into two subregions, and the blocks are divided into two groups and are placed in respective subregions. The two steps repeat until each subregion contains at most a given number of blocks, defined by the ratio of the total block area to the chip area. At last, we refine the floorplan by perturbing blocks inside a subregion as well as in different subregions. Compared with the B*-tree based floorplanner alone, our method is more efficient and obtains significantly better results, with an average cost of only 51.8 % of that obtained by using the B*-tree alone, based on a set of real industrial flip-chip designs provided by leading companies

Fracture Mechanics Method for Word Embedding Generation of Neural Probabilistic Linguistic Model

Word embedding, a lexical vector representation generated via the neural linguistic model (NLM), is empirically demonstrated to be appropriate for improvement of the performance of traditional language model. However, the supreme dimensionality that is inherent in NLM contributes to the problems of hyperparameters and long-time training in modeling. Here, we propose a force-directed method to improve such problems for simplifying the generation of word embedding. In this framework, each word is assumed as a point in the real world; thus it can approximately simulate the physical movement following certain mechanics. To simulate the variation of meaning in phrases, we use the fracture mechanics to do the formation and breakdown of meaning combined by a 2-gram word group. With the experiments on the natural linguistic tasks of part-of-speech tagging, named entity recognition and semantic role labeling, the result demonstrated that the 2-dimensional word embedding can rival the word embeddings generated by classic NLMs, in terms of accuracy, recall, and text visualization

A Techniques for Scalable and Effective Routability Evaluation

Routing congestion has become a critical layout challenge in nanoscale circuits since it is a critical factor in determining the routability of a design. An unroutable design is not useful even though it closes on all other design metrics. Fast design closure can only be achieved by accurately evaluating whether a design is routable or not early in the design cycle. Lately, it has become common to use a “light mode ” version of a global router to quickly evaluate the routability of a given placement. This approach suffers from three weaknesses: (i) it does not adequately model local routing resources, which can cause incorrect routability predictions that are only detected late, during detailed routing, (ii) the congestion maps obtained by it tend to have isolated hot spots surrounded by noncongested spots, called “noisy hot spots”, which further affects the accuracy in routability evaluation, (iii) the metrics used to represent congestion may yield numbers that do not provide sufficient intuition to the designer; moreover, they may often fail to predict the routability accurately. This paper presents solutions to these issues. First, we propose three approaches to model local routing resources. Second, we propose a smoothing technique to reduce the number of noisy hot spots and obtain a more accurate routability evaluation result. Finally, we develop a new metric which represents congestion maps with higher fidelity. We apply the proposed techniques to several industrial circuits and demonstrate that one can better predict and evaluate design routability, and congestion mitigation tools can perform muc