Heurísticas bioinspiradas para el problema de Floorplanning 3D térmico de dispositivos MPSoCs

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Informática, Departamento de Arquitectura de Computadores y Automática, leída el 20-06-2013Depto. de Arquitectura de Computadores y AutomáticaFac. de InformáticaTRUEunpu

Effective network grid synthesis and optimization for high performance very large scale integration system design

制度:新 ; 文部省報告番号:甲2642号 ; 学位の種類:博士(工学) ; 授与年月日:2008/3/15 ; 早大学位記番号:新480

Multiobjective strategies for New Product Development in the pharmaceutical industry

New Product Development (NPD) constitutes a challenging problem in the pharmaceutical industry, due to the characteristics of the development pipeline. Formally, the NPD problem can be stated as follows: select a set of R&D projects from a pool of candidate projects in order to satisfy several criteria (economic profitability, time to market) while coping with the uncertain nature of the projects. More precisely, the recurrent key issues are to determine the projects to develop once target molecules have been identified, their order and the level of resources to assign. In this context, the proposed approach combines discrete event stochastic simulation (Monte Carlo approach) with multiobjective genetic algorithms (NSGAII type, Non-Sorted Genetic Algorithm II) to optimize the highly combinatorial portfolio management problem. In that context, Genetic Algorithms (GAs) are particularly attractive for treating this kind of problem, due to their ability to directly lead to the so-called Pareto front and to account for the combinatorial aspect. This work is illustrated with a study case involving nine interdependent new product candidates targeting three diseases. An analysis is performed for this test bench on the different pairs of criteria both for the bi- and tricriteria optimization: large portfolios cause resource queues and delays time to launch and are eliminated by the bi- and tricriteria optimization strategy. The optimization strategy is thus interesting to detect the sequence candidates. Time is an important criterion to consider simultaneously with NPV and risk criteria. The order in which drugs are released in the pipeline is of great importance as with scheduling problems

Multiobjective strategies for New Product Development in the pharmaceutical industry

New Product Development (NPD) constitutes a challenging problem in the pharmaceutical industry, due to the characteristics of the development pipeline. Formally, the NPD problem can be stated as follows: select a set of R&D projects from a pool of candidate projects in order to satisfy several criteria (economic profitability, time to market) while coping with the uncertain nature of the projects. More precisely, the recurrent key issues are to determine the projects to develop once target molecules have been identified, their order and the level of resources to assign. In this context, the proposed approach combines discrete event stochastic simulation (Monte Carlo approach) with multiobjective genetic algorithms (NSGAII type, Non-Sorted Genetic Algorithm II) to optimize the highly combinatorial portfolio management problem. In that context, Genetic Algorithms (GAs) are particularly attractive for treating this kind of problem, due to their ability to directly lead to the so-called Pareto front and to account for the combinatorial aspect. This work is illustrated with a study case involving nine interdependent new product candidates targeting three diseases. An analysis is performed for this test bench on the different pairs of criteria both for the bi- and tricriteria optimization: large portfolios cause resource queues and delays time to launch and are eliminated by the bi- and tricriteria optimization strategy. The optimization strategy is thus interesting to detect the sequence candidates. Time is an important criterion to consider simultaneously with NPV and risk criteria. The order in which drugs are released in the pipeline is of great importance as with scheduling problems

Simulated Annealing

The book contains 15 chapters presenting recent contributions of top researchers working with Simulated Annealing (SA). Although it represents a small sample of the research activity on SA, the book will certainly serve as a valuable tool for researchers interested in getting involved in this multidisciplinary field. In fact, one of the salient features is that the book is highly multidisciplinary in terms of application areas since it assembles experts from the fields of Biology, Telecommunications, Geology, Electronics and Medicine

Energy-aware synthesis for networks on chip architectures

The Network on Chip (NoC) paradigm was introduced as a scalable communication infrastructure for future System-on-Chip applications. Designing application specific customized communication architectures is critical for obtaining low power, high performance solutions. Two significant design automation problems are the creation of an optimized configuration, given application requirement the implementation of this on-chip network. Automating the design of on-chip networks requires models for estimating area and energy, algorithms to effectively explore the design space and network component libraries and tools to generate the hardware description. Chip architects are faced with managing a wide range of customization options for individual components, routers and topology. As energy is of paramount importance, the effectiveness of any custom NoC generation approach lies in the availability of good energy models to effectively explore the design space. This thesis describes a complete NoC synthesis flow, called NoCGEN, for creating energy-efficient custom NoC architectures. Three major automation problems are addressed: custom topology generation, energy modeling and generation. An iterative algorithm is proposed to generate application specific point-to-point and packet-switched networks. The algorithm explores the design space for efficient topologies using characterized models and a system-level floorplanner for evaluating placement and wire-energy. Prior to our contribution, building an energy model required careful analysis of transistor or gate implementations. To alleviate the burden, an automated linear regression-based methodology is proposed to rapidly extract energy models for many router designs. The resulting models are cycle accurate with low-complexity and found to be within 10% of gate-level energy simulations, and execute several orders of magnitude faster than gate-level simulations. A hardware description of the custom topology is generated using a parameterizable library and custom HDL generator. Fully reusable and scalable network components (switches, crossbars, arbiters, routing algorithms) are described using a template approach and are used to compose arbitrary topologies. A methodology for building and composing routers and topologies using a template engine is described. The entire flow is implemented as several demonstrable extensible tools with powerful visualization functionality. Several experiments are performed to demonstrate the design space exploration capabilities and compare it against a competing min-cut topology generation algorithm

Embedded electronic systems driven by run-time reconfigurable hardware

Abstract This doctoral thesis addresses the design of embedded electronic systems based on run-time reconfigurable hardware technology –available through SRAM-based FPGA/SoC devices– aimed at contributing to enhance the life quality of the human beings. This work does research on the conception of the system architecture and the reconfiguration engine that provides to the FPGA the capability of dynamic partial reconfiguration in order to synthesize, by means of hardware/software co-design, a given application partitioned in processing tasks which are multiplexed in time and space, optimizing thus its physical implementation –silicon area, processing time, complexity, flexibility, functional density, cost and power consumption– in comparison with other alternatives based on static hardware (MCU, DSP, GPU, ASSP, ASIC, etc.). The design flow of such technology is evaluated through the prototyping of several engineering applications (control systems, mathematical coprocessors, complex image processors, etc.), showing a high enough level of maturity for its exploitation in the industry.Resumen Esta tesis doctoral abarca el diseño de sistemas electrónicos embebidos basados en tecnología hardware dinámicamente reconfigurable –disponible a través de dispositivos lógicos programables SRAM FPGA/SoC– que contribuyan a la mejora de la calidad de vida de la sociedad. Se investiga la arquitectura del sistema y del motor de reconfiguración que proporcione a la FPGA la capacidad de reconfiguración dinámica parcial de sus recursos programables, con objeto de sintetizar, mediante codiseño hardware/software, una determinada aplicación particionada en tareas multiplexadas en tiempo y en espacio, optimizando así su implementación física –área de silicio, tiempo de procesado, complejidad, flexibilidad, densidad funcional, coste y potencia disipada– comparada con otras alternativas basadas en hardware estático (MCU, DSP, GPU, ASSP, ASIC, etc.). Se evalúa el flujo de diseño de dicha tecnología a través del prototipado de varias aplicaciones de ingeniería (sistemas de control, coprocesadores aritméticos, procesadores de imagen, etc.), evidenciando un nivel de madurez viable ya para su explotación en la industria.Resum Aquesta tesi doctoral està orientada al disseny de sistemes electrònics empotrats basats en tecnologia hardware dinàmicament reconfigurable –disponible mitjançant dispositius lògics programables SRAM FPGA/SoC– que contribueixin a la millora de la qualitat de vida de la societat. S’investiga l’arquitectura del sistema i del motor de reconfiguració que proporcioni a la FPGA la capacitat de reconfiguració dinàmica parcial dels seus recursos programables, amb l’objectiu de sintetitzar, mitjançant codisseny hardware/software, una determinada aplicació particionada en tasques multiplexades en temps i en espai, optimizant així la seva implementació física –àrea de silici, temps de processat, complexitat, flexibilitat, densitat funcional, cost i potència dissipada– comparada amb altres alternatives basades en hardware estàtic (MCU, DSP, GPU, ASSP, ASIC, etc.). S’evalúa el fluxe de disseny d’aquesta tecnologia a través del prototipat de varies aplicacions d’enginyeria (sistemes de control, coprocessadors aritmètics, processadors d’imatge, etc.), demostrant un nivell de maduresa viable ja per a la seva explotació a la indústria

Cryptographic primitives on reconfigurable platforms.

Tsoi Kuen Hung.Thesis (M.Phil.)--Chinese University of Hong Kong, 2002.Includes bibliographical references (leaves 84-92).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Motivation --- p.1Chapter 1.2 --- Objectives --- p.3Chapter 1.3 --- Contributions --- p.3Chapter 1.4 --- Thesis Organization --- p.4Chapter 2 --- Background and Review --- p.6Chapter 2.1 --- Introduction --- p.6Chapter 2.2 --- Cryptographic Algorithms --- p.6Chapter 2.3 --- Cryptographic Applications --- p.10Chapter 2.4 --- Modern Reconfigurable Platforms --- p.11Chapter 2.5 --- Review of Related Work --- p.14Chapter 2.5.1 --- Montgomery Multiplier --- p.14Chapter 2.5.2 --- IDEA Cipher --- p.16Chapter 2.5.3 --- RC4 Key Search --- p.17Chapter 2.5.4 --- Secure Random Number Generator --- p.18Chapter 2.6 --- Summary --- p.19Chapter 3 --- The IDEA Cipher --- p.20Chapter 3.1 --- Introduction --- p.20Chapter 3.2 --- The IDEA Algorithm --- p.21Chapter 3.2.1 --- Cipher Data Path --- p.21Chapter 3.2.2 --- S-Box: Multiplication Modulo 216 + 1 --- p.23Chapter 3.2.3 --- Key Schedule --- p.24Chapter 3.3 --- FPGA-based IDEA Implementation --- p.24Chapter 3.3.1 --- Multiplication Modulo 216 + 1 --- p.24Chapter 3.3.2 --- Deeply Pipelined IDEA Core --- p.26Chapter 3.3.3 --- Area Saving Modification --- p.28Chapter 3.3.4 --- Key Block in Memory --- p.28Chapter 3.3.5 --- Pipelined Key Block --- p.30Chapter 3.3.6 --- Interface --- p.31Chapter 3.3.7 --- Pipelined Design in CBC Mode --- p.31Chapter 3.4 --- Summary --- p.32Chapter 4 --- Variable Radix Montgomery Multiplier --- p.33Chapter 4.1 --- Introduction --- p.33Chapter 4.2 --- RSA Algorithm --- p.34Chapter 4.3 --- Montgomery Algorithm - Ax B mod N --- p.35Chapter 4.4 --- Systolic Array Structure --- p.36Chapter 4.5 --- Radix-2k Core --- p.37Chapter 4.5.1 --- The Original Kornerup Method (Bit-Serial) --- p.37Chapter 4.5.2 --- The Radix-2k Method --- p.38Chapter 4.5.3 --- Time-Space Relationship of Systolic Cells --- p.38Chapter 4.5.4 --- Design Correctness --- p.40Chapter 4.6 --- Implementation Details --- p.40Chapter 4.7 --- Summary --- p.41Chapter 5 --- Parallel RC4 Engine --- p.42Chapter 5.1 --- Introduction --- p.42Chapter 5.2 --- Algorithms --- p.44Chapter 5.2.1 --- RC4 --- p.44Chapter 5.2.2 --- Key Search --- p.46Chapter 5.3 --- System Architecture --- p.47Chapter 5.3.1 --- RC4 Cell Design --- p.47Chapter 5.3.2 --- Key Search --- p.49Chapter 5.3.3 --- Interface --- p.50Chapter 5.4 --- Implementation --- p.50Chapter 5.4.1 --- RC4 cell --- p.51Chapter 5.4.2 --- Floorplan --- p.53Chapter 5.5 --- Summary --- p.53Chapter 6 --- Blum Blum Shub Random Number Generator --- p.55Chapter 6.1 --- Introduction --- p.55Chapter 6.2 --- RRNG Algorithm . . --- p.56Chapter 6.3 --- PRNG Algorithm --- p.58Chapter 6.4 --- Architectural Overview --- p.59Chapter 6.5 --- Implementation --- p.59Chapter 6.5.1 --- Hardware RRNG --- p.60Chapter 6.5.2 --- BBS PRNG --- p.61Chapter 6.5.3 --- Interface --- p.66Chapter 6.6 --- Summary --- p.66Chapter 7 --- Experimental Results --- p.68Chapter 7.1 --- Design Platform --- p.68Chapter 7.2 --- IDEA Cipher --- p.69Chapter 7.2.1 --- Size of IDEA Cipher --- p.70Chapter 7.2.2 --- Performance of IDEA Cipher --- p.70Chapter 7.3 --- Variable Radix Systolic Array --- p.71Chapter 7.4 --- Parallel RC4 Engine --- p.75Chapter 7.5 --- BBS Random Number Generator --- p.76Chapter 7.5.1 --- Size --- p.76Chapter 7.5.2 --- Speed --- p.76Chapter 7.5.3 --- External Clock --- p.77Chapter 7.5.4 --- Random Performance --- p.78Chapter 7.6 --- Summary --- p.78Chapter 8 --- Conclusion --- p.81Chapter 8.1 --- Future Development --- p.83Bibliography --- p.8