Algorithmic Approaches to the Steiner Problem in Networks

Das Steinerproblem in Netzwerken ist das Problem, in einem gewichteten Graphen eine gegebene Menge von Knoten kostenminimal zu verbinden. Es ist ein klassisches NP-schweres Problem und ein fundamentales Problem bei der Netzwerkoptimierung mit vielen praktischen Anwendungen. Wir nehmen dieses Problem mit verschiedenen Mitteln in Angriff: Relaxationen, die die Zulässigkeitsbedingungen lockern, um eine optimale Lösung annähern zu können; Heuristiken, um gute, aber nicht garantiert optimale Lösungen zu finden; und Reduktionen, um die Probleminstanzen zu vereinfachen, ohne eine optimale Lösung zu zerstören. In allen Fällen untersuchen und verbessern wir bestehende Methoden, stellen neue vor und evaluieren sie experimentell. Wir integrieren diese Bausteine in einen exakten Algorithmus, der den Stand der Algorithmik für die optimale Lösung dieses Problems darstellt. Viele der vorgestellten Methoden können auch für verwandte Probleme von Nutzen sein

A Finite Domain Constraint Approach for Placement and Routing of Coarse-Grained Reconfigurable Architectures

Scheduling, placement, and routing are important steps in Very Large Scale Integration (VLSI) design. Researchers have developed numerous techniques to solve placement and routing problems. As the complexity of Application Specific Integrated Circuits (ASICs) increased over the past decades, so did the demand for improved place and route techniques. The primary objective of these place and route approaches has typically been wirelength minimization due to its impact on signal delay and design performance. With the advent of Field Programmable Gate Arrays (FPGAs), the same place and route techniques were applied to FPGA-based design. However, traditional place and route techniques may not work for Coarse-Grained Reconfigurable Architectures (CGRAs), which are reconfigurable devices offering wider path widths than FPGAs and more flexibility than ASICs, due to the differences in architecture and routing network. Further, the routing network of several types of CGRAs, including the Field Programmable Object Array (FPOA), has deterministic timing as compared to the routing fabric of most ASICs and FPGAs reported in the literature. This necessitates a fresh look at alternative approaches to place and route designs. This dissertation presents a finite domain constraint-based, delay-aware placement and routing methodology targeting an FPOA. The proposed methodology takes advantage of the deterministic routing network of CGRAs to perform a delay aware placement

Machine Learning for AI-Augmented Design Space Exploration of Computer Systems

Advanced and emerging computer systems, ranging from supercomputers to embedded systems, feature high performance, energy efficiency, acceleration, and specialization. Design of such systems involves ever-increasing circuit complexity and architectural diversity. Commercial high-end processors, realized as very-large-scale integration circuits, have integrated exponentially increasing number of transistors on a chip over many decades. Along with the evolution of semiconductor manufacturing technology, another driving force behind the progress of processors has been the development of computer-aided design (CAD) software tools. Logic synthesis and physical design (LSPD) tool-chains allow designers to describe the computer system at the register-transfer level of abstraction and automatically convert the description into an integration circuit layout. The slowdown of technology scaling, on the other hand, has motivated the emergence of dark silicon and heterogeneous architectures with application-specific hardware accelerators. Design of various accelerators is facilitated by high-level synthesis (HLS) tools that translate a behavioral description of a computer system into a structural register-transfer level one. CAD approaches have evolved towards raising the level of design abstraction and providing more options to optimize the architecture. For each system synthesized via advanced CAD tools, designers explore the design space in search of optimal configurations of the tool options and architectural choices, also called . These knobs affect the execution of CAD algorithms and eventually impact the multi-dimensional -- () of the final implementation. During design-space exploration (DSE), designers leverage their experience and expertise pertaining to determining the relationship between knobs and QoR. To further reduce the number of time and resource consuming CAD runs during DSE, a large number of heuristic and model-based approaches have been proposed. More recently, the rise of machine learning (ML) and artificial intelligence (AI) has prompted the possibility of AI-augmented DSE which exploits ML techniques to predict the knobs-QoR relationship. Yet, existing heuristic and ML-based approaches still require a sufficient number of CAD runs for each system because they do not accumulate and exploit experiential knowledge across the systems as designers would do. To expand the potential of AI-augmented DSE and push the frontier forward, multiple challenges arise due to the characteristics of CAD flows. 1) Whereas many ML applications utilize data obtained from huge collections of users' input and public databases for a single problem, the QoR-prediction problem for each system suffers from limited availability of data obtained from expensive CAD runs. Especially, an industrial LSPD tool-chain specifies hundreds of separate knobs, resulting in an extreme curse of dimensionality. 2) Different systems exhibit different knobs-QoR relationship. Hence, learning from previously explored systems needs to be preceded by identifying distinct systems and relating them to one another. Often, it is difficult to obtain an efficient representation of a system. 3) Designers often apply different sets of knob configurations to different systems, which makes it harder to learn from previous DSE results. Especially in HLS, the heterogeneity of various systems leads to broad knob heterogeneity across them. To address these challenges and boost the ML performance, I propose to flexibly connect the elements of the many QoR-prediction problems with one another. My thesis is that . For LSPD of industrial high-performance processors, I propose a novel collaborative recommender system approach that learns hidden features from the interactions (CAD runs) of many \textit{users} (systems) and \textit{items} (knob configurations). To cope with the curse of dimensionality, the item features are decomposed into features of item attributes (knobs). The combined model predicts QoR for each user-item pair. For HLS of application-specific accelerators, I present a series of neural network models in the order of evolution towards the proposed mixed-sharing \textit{transfer learning} model. Transfer learning aims at leveraging knowledge gained from previous problems; however, due to the system and knob heterogeneities, the model needs to distinguish which piece of that knowledge should be transferred. The proposed ML approaches aim to not only use experiential knowledge as designers do but also to ultimately assist designers by providing alternative insights and suggesting optimization possibilities for new systems. As an effort in this direction, I develop an AI-augmented DSE tool that exploits the aforementioned models and \textit{generates} recommended knob configurations for new target systems. Through this research, I investigate the potential of next-level AI-augmented DSE with the goal of promoting secure collaborative engineering in the CAD community without the need of sharing confidential information and intellectual properties

LIPIcs, Volume 261, ICALP 2023, Complete Volume

LIPIcs, Volume 261, ICALP 2023, Complete Volum

Across Space and Time. Papers from the 41st Conference on Computer Applications and Quantitative Methods in Archaeology, Perth, 25-28 March 2013

This volume presents a selection of the best papers presented at the forty-first annual Conference on Computer Applications and Quantitative Methods in Archaeology. The theme for the conference was "Across Space and Time", and the papers explore a multitude of topics related to that concept, including databases, the semantic Web, geographical information systems, data collection and management, and more

LIPIcs, Volume 274, ESA 2023, Complete Volume

LIPIcs, Volume 274, ESA 2023, Complete Volum

Architecture of surface : the significance of surficial thought and topological metaphors of design

In the early twentieth century, the modernists problematized ornament in their refashioning of architecture for the industrial age. Today, architects are formulating different responses to image and its (re)production in the information age. In both discourses of ornament and image, surfaces are often the perpetrators: visual boundaries that facilitate false appearances, imprisoning humanity in a shadowy cave of illusion. Such views follow a familiar metaphysical model characterized by the opposition between inside and outside and the opaque boundary that acts as a barrier. This model determines the traditional (Platonic) philosophical approach that follows a distinct hierarchical order and a perpendicular movement of thought that seeks to penetrate appearances in order to arrive at the essence of things. This thesis deploys Gilles Deleuze’s philosophy to advance a different understanding of surface, image and appearance in architecture. Using the Bilbao Guggenheim Museum as a catalyst, the thesis argues that many of the concepts with which commentators and critics analyse contemporary architecture follow models of thought that consider surfaces and their effects as secondary categories. Given the significance of visual (re)production and communication for contemporary society, the thesis proposes a different model based on surface as that which simultaneously produces, connects and separates image and reality. This non-hierarchical approach is inspired by surficial philosophy, which relates to Earth, to geology and topology, conjuring up a diversity of concepts from the thickness of the crust to the smooth fluidity of the seas. The result is an unfamiliar, polemical model of thought that does not define surface as a limit or barrier, rather a medium, a pliable space of smooth mixture. In this model, difference is not in the opposition between the two sides of a boundary line, rather it occurs upon and within the surficial landscape that consumes categories, promoting nomadic movements of thought that offer greater flexibility towards creativity and new possibilities. In surficial thought, images and appearances are not artificial copies of an originary reality, rather they possess a unique reality of their own. This approach allows architectural imagery to be theorised as a positive surfacing of architecture beyond disciplinary lines and the locality of a specific time and place