Many digital signal processing and real-time streaming systems are modeled using dataflow graphs, such as Synchronous Dataflow (SDF) and Cyclo-static Dataflow (CSDF) graphs that allow static analysis and optimization techniques. However, mapping of such descriptions into tightly constrained real-time implementations requires optimization of resource sharing, buffering and scheduling across a multi-dimensional latency-throughput-area objective space. This requires techniques that can find the Pareto-optimal set of implementations for the designer to choose from. In this work, we address the problem of multi-objective mapping and scheduling of SDF and CSDF graphs onto heterogeneous multi-processor platforms. Building on previous work, this thesis extends existing two-stage hybrid heuristics that combine an evolutionary algorithm with an integer linear programming (ILP) model to jointly optimize throughput, area and latency for SDF graphs. The primary contributions of this work include: (1) extension of the ILP model to support CSDFGs with additional buffer size optimizations; (2) a further optimization in the ILP-based scheduling model to achieve a runtime speedup of almost a factor of 10 compared to the existing SDFG formulation; (3) a list scheduling heuristic that replaces the ILP model in the hybrid heuristic to generate Pareto-optimal solutions at significantly decreased runtime while maintaining near-optimality of the solutions within an acceptable gap of 10% when compared to its ILP counterparts. The list scheduling heuristic presented in this work is based on existing modulo scheduling approaches for software pipelining in the compiler domain, but has been extended by introducing a new concept of mobility-based rescheduling before resorting to backtracking. It has been proved in this work that if mobility-based rescheduling is performed, the number of required backtrackings and hence overall complexity and runtime is less. / text

With the increase of device complexity and test-data volume required to guarantee adequate defect coverage, external testing is becoming increasingly difficult and expensive. Logic Built-in Self Test (LBIST) is a viable alternative test strategy as it helps reduce dependence on an elaborate external test equipment, enables the application of a large number of random tests, and allows for at-speed testing. The main problem with LBIST is suboptimal fault coverage achievable with random vectors. LFSR reseeding is used to increase the coverage. However, to achieve satisfactory coverage, one often needs a large number of seeds. Computing a small number of seeds for LBIST reseeding still remains a tremendous challenge, since the vectors needed to detect all faults may be spread across the huge LFSR vector space. In this work, we propose new methods to enable the computation of a small number of LFSR seeds to cover all stuck-at faults as a first-order satisfiability problem involving extended theories. We present a technique based on SMT (Satisfiability Modulo Theories) with the theory of bit-vectors to combine the tasks of test-generation and seed computation. We describe a seed reduction flow which is based on the `chaining' of faults instead of pre-computed vectors. We experimentally demonstrate that our method can produce very small sets of seeds for complete stuck-at fault coverage. Additionally, we present methods for inserting test-points to enhance the testability of a circuit in such a way as to allow even further reduction in the number of seeds. / Master of Science

Title: Project portfolio optimization with time and resources Author: Bc. Tomáš Huml Department: Department of Theoretical Computer Science and Mathematical Logic Supervisor: Doc. RNDr. Roman Barták, Ph.D Abstract: Traditional project portfolio optimization deals with static projects that are not evolving in time. The focus of this diploma thesis is on projects that are spread in time, typically such projects consists of a sequence (or other partially ordered structure) of actions that require some resources (money, people, etc.) for realization. Then the project portfolio optimization deals with selecting a subset of projects according to given time and space (resource) restrictions and optimizing certain criteria such as overall profit. This problem is very close to oversubscribed scheduling where the most profitable subset of orders is being scheduled. Hence scheduling techniques will be the main inspiration for solving this new type of problems. Lots of modelling algorithms for optimal portfolio selection are proposed in this diploma thesis and several of them are implemented in a program which is part of this thesis as well. Keywords: portfolio optimization, integer linear programming (ILP), workflow optimization, project interdependencies

This thesis focuses on the survivable routing problem in WDM mesh networks where the objective is to minimize the total number of wavelengths used for establishing working and protection paths in the WDM networks. The past studies for survivable routing suffers from the scalability problem when the number of nodes/links or connection requests grow in the network. In this thesis, a novel path based shared protection framework namely Inter-Group Shared protection (I-GSP) is proposed where the traffic matrix can be divided into multiple protection groups (PGs) based on specific grouping policy. Optimization is performed on these PGs such that sharing of protection wavelengths is considered not only inside a PG, but between the PGs. Simulation results show that I-GSP based integer linear programming model, namely, ILP-II solves the networks in a reasonable amount of time for which a regular integer linear programming formulation, namely, ILP-I becomes computationally intractable. For most of the cases the gap between the optimal solution and the ILP-II ranges between (2-16)%. The proposed ILP-II model yields a scalable solution for the capacity planning in the survivable optical networks based on the proposed I-GSP protection architecture.

Any unexpected disruption to WDM (Wavelength Division Multiplexing) based optical networks which carry data traffic at tera-bit per second may result in a huge loss to its end-users and the carrier itself. Thus survivability has been well-recognized as one of the most important objectives in the design of optical Internet. This thesis proposes a novel survivable routing architecture for the optical Internet. We focus on a number of key issues that are essential to achieve the desired service scenarios, including the tasks of (a) minimizing the total number of wavelengths used for establishing working and protection paths in WDM networks; (b) minimizing the number of affected working paths in case of a link failure; (c) handling large scale WDM mesh networks; and (d) supporting both Quality of Service (QoS) and best-effort based working lightpaths. To implement the above objectives, a novel path based shared protection framework namely Group Shared protection (GSP) is proposed where the traffic matrix can be divided into multiple protection groups (PGs) based on specific grouping policy, and optimization is performed on these PGs. To the best of our knowledge this is the first work done in the area of group based WDM survivable routing approaches where not only the resource sharing is conducted among the PGs to achieve the best possible capacity efficiency, but also an integrated survivable routing framework is provided by incorporating the above objectives. Simulation results show the effectiveness of the proposed schemes.

This thesis focuses on the survivable routing problem in WDM mesh networks where the objective is to minimize the total number of wavelengths used for establishing working and protection paths in the WDM networks. The past studies for survivable routing suffers from the scalability problem when the number of nodes/links or connection requests grow in the network. In this thesis, a novel path based shared protection framework namely Inter-Group Shared protection (I-GSP) is proposed where the traffic matrix can be divided into multiple protection groups (PGs) based on specific grouping policy. Optimization is performed on these PGs such that sharing of protection wavelengths is considered not only inside a PG, but between the PGs. Simulation results show that I-GSP based integer linear programming model, namely, ILP-II solves the networks in a reasonable amount of time for which a regular integer linear programming formulation, namely, ILP-I becomes computationally intractable. For most of the cases the gap between the optimal solution and the ILP-II ranges between (2-16)%. The proposed ILP-II model yields a scalable solution for the capacity planning in the survivable optical networks based on the proposed I-GSP protection architecture.

The demand for devices like Personal Digital Assistants (PDA’s), Laptops, Smart Mobile Phones, are at an all time high. As the demand for these devices increases, so is the push to provide sophisticated functionalities in these devices. However energy consumption has become a major constraint in providing increased functionality for these devices. A majority of the applications meant for these devices are rich with multimedia content. In this thesis, we propose two approaches for compiler directed energy reduction, one targeting the memory subsystem and another the processor. The first technique is a compiler directed optimization technique that reduces the energy consumption of the memory subsystem, for an off-chip partitioned memory archi- tecture, having multiple memory banks, and various low-power operating modes for each of these banks. We propose an efficient layout of the data segment to reduce the number of simultaneously active memory banks, so that the other memory banks that are inactive can be put to low power modes to reduce the energy. We model this problem as a graph partitioning problem, and use well known heuristics to solve the same. We also propose a simple Integer Linear Programming (ILP) formulation for the above problem. Perfor- mance results indicate that our approach achieves an energy reduction of 20% compared to the base scheme, and a reduction of 8%-10% over a previously suggested method. Also, our results are well within the optimal results obtained by using ILP method. The second approach proposed in this thesis reduces the dynamic energy consumed by the processor using dynamic voltage and frequency scaling technique. Earlier works on dynamic voltage scaling focused mainly on performing voltage scaling when the CPU is waiting for memory subsystem or concentrated chiefly on loop nests and/or subroutine calls having sufficient number of dynamic instructions. We concentrate on coarser pro- gram regions and for the first time uses program phase behavior for performing dynamic voltage scaling. We relate the Dynamic Voltage Scaling Problem to the Multiple Choice Knapsack Problem, and use well known heuristics to solve it efficiently. Also, we develop a simple Integer Linear Programming (ILP) problem formulation for this problem. Experi-mental evaluation on a set of media applications reveal that our heuristic method obtains 35-40% reduction in energy consumption on an average, with a negligible performance degradation. Further the energy consumed by our heuristic solution is within 1% the optimal solution obtained by the ILP approach.

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Any unexpected disruption to WDM (Wavelength Division Multiplexing) based optical networks which carry data traffic at tera-bit per second may result in a huge loss to its end-users and the carrier itself. Thus survivability has been well-recognized as one of the most important objectives in the design of optical Internet. This thesis proposes a novel survivable routing architecture for the optical Internet. We focus on a number of key issues that are essential to achieve the desired service scenarios, including the tasks of (a) minimizing the total number of wavelengths used for establishing working and protection paths in WDM networks; (b) minimizing the number of affected working paths in case of a link failure; (c) handling large scale WDM mesh networks; and (d) supporting both Quality of Service (QoS) and best-effort based working lightpaths. To implement the above objectives, a novel path based shared protection framework namely Group Shared protection (GSP) is proposed where the traffic matrix can be divided into multiple protection groups (PGs) based on specific grouping policy, and optimization is performed on these PGs. To the best of our knowledge this is the first work done in the area of group based WDM survivable routing approaches where not only the resource sharing is conducted among the PGs to achieve the best possible capacity efficiency, but also an integrated survivable routing framework is provided by incorporating the above objectives. Simulation results show the effectiveness of the proposed schemes.