Deep reinforcement learning on 1-layer circuit routing problem

In VLSI design, routing is the step that determines the paths for circuit nets and interconnections. While routing can be a very complex process involving time, congestion and space information, the problem can be modelled as a maze routing problem. In specific, given a 2d array and a set of start nodes and end nodes, the agent is trying to optimize the solution by connectivity and path length. Traditionally, the routing problem is solved using graph search techniques such as Lee’s algorithm. The result produced by graph search algorithms relies heavily on the order of routing. While some simple heuristics are available, the result is not stable because simple heuristics take greedy approaches and neglect the long-term reward. The recent development of deep learning, especially deep reinforcement learning, can be a good approach to finding better ordering on attacking the routing problem. We introduce a reinforcement learning approach to the traditional 2-point nets in 1-layer maze routing problem

Searching and ranking in entity-relationship graphs

The Web bears the potential to become the world';s most comprehensive knowledge base. Organizing information from the Web into entity-relationship graph structures could be a first step towards unleashing this potential. In a second step, the inherent semantics of such structures would have to be exploited by expressive search techniques that go beyond today';s keyword search paradigm. In this realm, as a first contribution of this thesis, we present NAGA (Not Another Google Answer), a new semantic search engine. NAGA provides an expressive, graph-based query language that enables queries with entities and relationships. The results are retrieved based on subgraph matching techniques and ranked by means of a statistical ranking model. As a second contribution, we present STAR (Steiner Tree Approximation in Relationship Graphs), an efficient technique for finding "close'; relations (i.e., compact connections) between k(> 2) entities of interest in large entity-relationship graphs. Our third contribution is MING (Mining Informative Graphs). MING is an efficient method for retrieving "informative'; subgraphs for k(> 2) entities of interest from an entity-relationship graph. Intuitively, these would be subgraphs that can explain the relations between the k entities of interest. The knowledge discovery tasks supported by MING have a stronger semantic flavor than the ones supported by STAR. STAR and MING are integrated into the query answering component of the NAGA engine. NAGA itself is a fully implemented prototype system and is part of the YAGONAGA project.Das Web birgt in sich das Potential zur umfangreichsten Wissensbasis der Welt zu werden. Das Organisieren der Information aus dem Web in Entity-Relationship-Graphstrukturen könnte ein erster Schritt sein, um dieses Potential zu entfalten. In einem zweiten Schritt müssten ausdrucksstarke Suchtechniken entwickelt werden, die über das heutige Keyword-basierte Suchparadigma hinausgehen und die inhärente Semantik solcher Strukturen ausnutzen. In diesem Rahmen stellen wir als ersten Beitrag dieser Arbeit NAGA (Not Another Google Answer) vor, eine neue semantische Suchmaschine. NAGA bietet eine ausdrucksstarke, graphbasierte Anfragesprache, die Anfragen mit Entitäten und Relationen ermöglicht. Die Ergebnisse werden durch Subgraph-Matching-Techniken gefunden und mithilfe eines statistischen Modells in eine Rangliste gebracht. Als zweiten Beitrag stellen wir STAR (Steiner Tree Approximation in Relationship Graphs) vor, eine effiziente Technik, um "nahe'; Relationen (d.h. kompakte Verbindungen) zwischen k(> 2) Entitäten in großen Entity-Relationship-Graphen zu finden. Unser dritter Beitrag ist MING (Mining Informative Graphs). MING ist eine effiziente Methode, die das Finden von "informativen'; Subgraphen für k(> 2) Entitäten aus einem Entity-Relationship-Graphen ermöglicht. Dies sind Subgraphen, die die Beziehungen zwischen den k Entitäten erklären können. Im Vergleich zu STAR unterstützt MING Aufgaben der Wissensexploration, die einen stärkeren semantischen Charakter haben. Sowohl STAR als auch MING sind in die Query-Answering-Komponente der NAGA-Suchmaschine integriert. NAGA selbst ist ein vollständig implementiertes Prototypsystem und Teil des YAGO-NAGA-Projekts

Local Search for Final Placement in VLSI Design

A new heuristic is presented for the general cell placement problem where the objective is to minimize total bounding box netlength. The heuristic is based on the Guided Local Search (GLS) metaheuristic. GLS modifies the objective function in a constructive way to escape local minima. Previous attempts to use local search on final (or detailed) placement problems have often failed as the neighborhood quickly becomes too excessive for large circuits. Nevertheless, by combining GLS with Fast Local Search it is possible to focus the search on appropriate sub-neighborhoods, thus reducing the time complexity considerably