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Manufacturing technology has permitted an exponential growth in transistor count and density. However, making efficient use of the available transistors in the design has become exceedingly difficult. Standard design flow involves synthesis, verification, placement and routing followed by final tape out of the design. Due to the presence of various undesirable effects like capacitive crosstalk, supply noise, high temperatures, etc., verification/validation of the design has become a challenging problem. Therefore, having a good design convergence may not be possible within the target time, due to a need for a large number of design iterations. Capacitive crosstalk is one of the major causes of design convergence problems in deep sub-micron era. With scaling, the number of crosstalk violations has been increasing because of reduced inter-wire distances. Consequently only the most severe crosstalk faults are fixed pre-silicon while the rest are tested post-silicon. Testing for capacitive crosstalk involves generation of input patterns which can be applied post-silicon to the integrated circuit and comparison of the output response. These patterns are generated at the gate/Register Transfer Level (RTL) of abstraction using Automatic Test Pattern Generation (ATPG) tools. In this dissertation, an Integer Linear Programming (ILP) based ATPG technique for maximizing crosstalk induced delay increase at the victim net, for multiple aggressor crosstalk faults, is presented. Moreover, various solutions for pattern generation considering both zero as well as unit delay models is also proposed. With voltage scaling, power supply switching noise has become one of the leading causes of signal integrity related failures in deep sub-micron designs. Hence, during power supply network design and analysis of power supply switching noise, computation of peak supply current is an essential step. Traditional peak current estimation approaches involve addition of peak current associated with all the CMOS gates which are switching in a combinational circuit. Consequently, this approach does not take the Boolean and temporal relationships of the circuit into account. This work presents an ILP based technique for generation of an input pattern pair which maximizes switching supply currents for a combinational circuit in the presence of integer gate delays. The input pattern pair generated using the above approach can be applied post-silicon for power droop testing. With high level of integration, Multi-Processor Systems on Chip (MPSoC) feature multiple processor cores and accelerators on the same die, so as to exploit the instruction level parallelism in the application. For hardware-software co-design, application programming model is based on a Task Graph, which represents task dependencies and execution/transfer times for various threads and processes within an application. Mapping an application to an MPSoC traditionally involves representing it in the form of a task graph and employing static scheduling in order to minimize the schedule length. Dynamic system behavior is not taken into consideration during static scheduling, while dynamic scheduling requires the knowledge of task graph at runtime. A run-time task graph extraction heuristic to facilitate dynamic scheduling is also presented here. A novel game theory based approach uses this extracted task graph to perform run-time scheduling in order to minimize total schedule length. With increase in transistor density, power density has gone up substantially. This has lead to generation of regions with very high temperature called Hotspots. Hotspots lead to reliability and performance issues and affect design convergence. In current generation Integrated Circuits (ICs) temperature is controlled by reducing power dissipation using Dynamic Thermal Management (DTM) techniques like frequency and/or voltage scaling. These techniques are reactive in nature and have detrimental effects on performance. Here, a look-ahead based task migration technique is proposed, in order to utilize the multitude of cores available in an MPSoC to eliminate thermal emergencies. Our technique is based on temperature prediction, leveraging upon a novel wavelet based thermal modeling approach. Hence, this work addresses several optimization problems that can be reduced to constrained max-satisfiability, involving integer as well as Boolean constraints in hardware and software domains. Moreover, it provides domain specific heuristic solutions for each of them.

The Internet is an example of a successful and scalable decentralized system capable of connecting millions of systems and transporting data seamlessly between them. It has been so successful that today, it is impossible to imagine entertainment, education, communication, business and other services without the Internet. In fact, the Internet is widely considered to be just another utility service. Additionally, the diversity of the end-systems ranging from high-end servers to mobile phones and sensors is only adding to the rate of its growth and value. This success is largely the result of careful thought put into the design philosophy of the Internet (globally deployed network layer, isolation of protocol layers). This has resulted in a digital information explosion with recent studies predicting a ten fold increase in the amount of digital content over the next five years. Factors such as information replication, increasingly affordable and heterogeneous end-systems, cheap storage and numerous services are cited to be a few of the reasons for this rapid growth. However, this fixed network layer also poses a barrier to introduction of innovations to support increasingly diverse end-systems and new communication paradigms. Moreover, the inherent issues in security, mobility, performance and reliability cannot be completely resolved by merely changing functionality in the end-systems, and will require addition of functionality in the core of the network as well. Service-centric networking is a new paradigm that seeks to introduce functionality into the network by deploying customized in-network services on-demand. Different compositions of services are used to customize connections to satisfy various user communication requirements. This work addresses four challenges in the context of service-centric networks: (1) automated service composition (2) combined service composition and routing, (3) support for inter-domain data-plane policies in such networks, and (4) end-system support for services through abstractions. Automated service composition deals with the challenge of finding an optimal sequence of services to satisfy communication requirements of a connection. This composed sequence of services is applied to the connection in the data-path. A semantic tree is used to describe communication characteristics and the problem is solved by reducing it to a planning problem. Service composition is typically followed by “service routing”, where the connection is set up such that the services are applied in order. This is not always optimal as we show through experiments. Combined service composition and routing tries to solve both problems in a single stage by reducing it to a planning problem. We further explore the issues of inter-domain data-plane policies in next-generation networks and discuss a system that uses the semantic tree to specify such policies. The system translates these policies into planning rules and determines the right way to set up the connection such that all policies are met. We also discuss the design and implementation of a novel “service socket” API that allows end-system applications to access services in a service-centric networking context. Another key aspect of next-generation networking is virtualization of the physical network infrastructure. Network virtualization allows multiple networks with different protocol stacks to share the same physical infrastructure. A key problem for virtual network providers is the need to efficiently allocate their customers' virtual network requests to the underlying network infrastructure. This problem is known to be computationally intractable and heuristic solutions continue to be developed. Most existing heuristics use a two-stage approach in which virtual nodes are first placed on physical nodes and virtual links are subsequently mapped. We present VHub, a novel single-stage approach that formulates this problem as a p-hub median problem. Our results show that VHub outperforms the state of the art algorithms by mapping 23% more virtual networks in lesser time (26% to 96%). Overall, this dissertation discusses techniques through which data-path customization can be achieved in next-generation networks. To solve some of the technical challenges, this work follows a cross-disciplinary approach exploring ideas from computer networking, distributed systems and algorithms to graph theory, mathematical optimization and artificial intelligence. The solutions are also tested through simulations using real and synthetically generated workloads to validate the design.

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