Custom Integrated Circuits

Contains reports on twelve research projects.Analog Devices, Inc.International Business Machines, Inc.Joint Services Electronics Program (Contract DAAL03-86-K-0002)Joint Services Electronics Program (Contract DAAL03-89-C-0001)U.S. Air Force - Office of Scientific Research (Grant AFOSR 86-0164)Rockwell International CorporationOKI Semiconductor, Inc.U.S. Navy - Office of Naval Research (Contract N00014-81-K-0742)Charles Stark Draper LaboratoryNational Science Foundation (Grant MIP 84-07285)National Science Foundation (Grant MIP 87-14969)Battelle LaboratoriesNational Science Foundation (Grant MIP 88-14612)DuPont CorporationDefense Advanced Research Projects Agency/U.S. Navy - Office of Naval Research (Contract N00014-87-K-0825)American Telephone and TelegraphDigital Equipment CorporationNational Science Foundation (Grant MIP-88-58764

Rich Socio-Cognitive Agents for Immersive Training Environments: Case of NonKin Village

Demand is on the rise for scientifically based human-behavior models that can be quickly customized and inserted into immersive training environments to recreate a given society or culture. At the same time, there are no readily available science model-driven environments for this purpose (see survey in Sect. 2). In researching how to overcome this obstacle, we have created rich (complex) socio-cognitive agents that include a large number of social science models (cognitive, sociologic, economic, political, etc) needed to enhance the realism of immersive, artificial agent societies. We describe current efforts to apply model-driven development concepts and how to permit other models to be plugged in should a developer prefer them instead. The current, default library of behavioral models is a metamodel, or authoring language, capable of generating immersive social worlds. Section 3 explores the specific metamodels currently in this library (cognitive, socio-political, economic, conversational, etc.) and Sect. 4 illustrates them with an implementation that results in a virtual Afghan village as a platform-independent model. This is instantiated into a server that then works across a bridge to control the agents in an immersive, platform-specific 3D gameworld (client). Section 4 also provides examples of interacting in the resulting gameworld and some of the training a player receives. We end with lessons learned and next steps for improving both the process and the gameworld. The seeming paradox of this research is that as agent complexity increases, the easier it becomes for the agents to explain their world, their dilemmas, and their social networks to a player or trainee

An asynchronous instruction length decoder

Journal ArticleAbstract-This paper describes an investigation of potential advantages and pitfalls of applying an asynchronous design methodology to an advanced microprocessor architecture. A prototype complex instruction set length decoding and steering unit was implemented using self-timed circuits. [The Revolving Asynchronous Pentium® Processor Instruction Decoder (RAPPID) design implemented the complete Pentium II® 32-bit MMX instruction set.] The prototype chip was fabricated on a 0.25-CMOS process and tested successfully. Results show significant advantages-in particular, performance of 2.5-4.5 instructions per nanosecond-with manageable risks using this design technology. The prototype achieves three times the throughput and half the latency, dissipating only half the power and requiring about the same area as the fastest commercial 400-MHz clocked circuit fabricated on the same process

Evolutionary algorithms for synthesis and optimisation of sequential logic circuits

Considerable progress has been made recently 1n the understanding of combinational logic optimization. Consequently a large number of university and industrial Electric Computing Aided Design (ECAD) programs are now available for optimal logic synthesis of combinational circuits. The progress with sequential logic synthesis and optimization, on the other hand, is considerably less mature. In recent years, evolutionary algorithms have been found to be remarkably effective way of using computers for solving difficult problems. This thesis is, in large part, a concentrated effort to apply this philosophy to the synthesis and optimization of sequential circuits. A state assignment based on the use of a Genetic Algorithm (GA) for the optimal synthesis of sequential circuits is presented. The state assignment determines the structure of the sequential circuit realizing the state machine and therefore its area and performances. The synthesis based on the GA approach produced designs with the smallest area to date. Test results on standard fmite state machine (FS:M) benchmarks show that the GA could generate state assignments, which required on average 15.44% fewer gates and 13.47% fewer literals compared with alternative techniques. Hardware evolution is performed through a succeSSlOn of changes/reconfigurations of elementary components, inter-connectivity and selection of the fittest configurations until the target functionality is reached. The thesis presents new approaches, which combine both genetic algorithm for state assignment and extrinsic Evolvable Hardware (EHW) to design sequential logic circuits. The implemented evolutionary algorithms are able to design logic circuits with size and complexity, which have not been demonstrated in published work. There are still plenty of opportunities to develop this new line of research for the synthesis, optimization and test of novel digital, analogue and mixed circuits. This should lead to a new generation of Electronic Design Automation tools.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Evolutionary algorithms for synthesis and optimisation of sequential logic circuits.

Considerable progress has been made recently 1n the understanding ofcombinational logic optimization. Consequently a large number of universityand industrial Electric Computing Aided Design (ECAD) programs are nowavailable for optimal logic synthesis of combinational circuits. The progresswith sequential logic synthesis and optimization, on the other hand, isconsiderably less mature.In recent years, evolutionary algorithms have been found to be remarkablyeffective way of using computers for solving difficult problems. This thesis is,in large part, a concentrated effort to apply this philosophy to the synthesisand optimization of sequential circuits.A state assignment based on the use of a Genetic Algorithm (GA) for theoptimal synthesis of sequential circuits is presented. The state assignmentdetermines the structure of the sequential circuit realizing the state machineand therefore its area and performances. The synthesis based on the GAapproach produced designs with the smallest area to date. Test results onstandard fmite state machine (FS:M) benchmarks show that the GA couldgenerate state assignments, which required on average 15.44% fewer gatesand 13.47% fewer literals compared with alternative techniques.Hardware evolution is performed through a succeSSlOn ofchanges/reconfigurations of elementary components, inter-connectivity andselection of the fittest configurations until the target functionality is reached.The thesis presents new approaches, which combine both genetic algorithmfor state assignment and extrinsic Evolvable Hardware (EHW) to designsequential logic circuits. The implemented evolutionary algorithms are able todesign logic circuits with size and complexity, which have not beendemonstrated in published work.There are still plenty of opportunities to develop this new line of research forthe synthesis, optimization and test of novel digital, analogue and mixedcircuits. This should lead to a new generation of Electronic DesignAutomation tools

Cellular Automata in Cryptographic Random Generators

Cryptographic schemes using one-dimensional, three-neighbor cellular automata as a primitive have been put forth since at least 1985. Early results showed good statistical pseudorandomness, and the simplicity of their construction made them a natural candidate for use in cryptographic applications. Since those early days of cellular automata, research in the field of cryptography has developed a set of tools which allow designers to prove a particular scheme to be as hard as solving an instance of a well-studied problem, suggesting a level of security for the scheme. However, little or no literature is available on whether these cellular automata can be proved secure under even generous assumptions. In fact, much of the literature falls short of providing complete, testable schemes to allow such an analysis. In this thesis, we first examine the suitability of cellular automata as a primitive for building cryptographic primitives. In this report, we focus on pseudorandom bit generation and noninvertibility, the behavioral heart of cryptography. In particular, we focus on cyclic linear and non-linear automata in some of the common configurations to be found in the literature. We examine known attacks against these constructions and, in some cases, improve the results. Finding little evidence of provable security, we then examine whether the desirable properties of cellular automata (i.e. highly parallel, simple construction) can be maintained as the automata are enhanced to provide a foundation for such proofs. This investigation leads us to a new construction of a finite state cellular automaton (FSCA) which is NP-Hard to invert. Finally, we introduce the Chasm pseudorandom generator family built on this construction and provide some initial experimental results using the NIST test suite

Cellular Automata

Modelling and simulation are disciplines of major importance for science and engineering. There is no science without models, and simulation has nowadays become a very useful tool, sometimes unavoidable, for development of both science and engineering. The main attractive feature of cellular automata is that, in spite of their conceptual simplicity which allows an easiness of implementation for computer simulation, as a detailed and complete mathematical analysis in principle, they are able to exhibit a wide variety of amazingly complex behaviour. This feature of cellular automata has attracted the researchers' attention from a wide variety of divergent fields of the exact disciplines of science and engineering, but also of the social sciences, and sometimes beyond. The collective complex behaviour of numerous systems, which emerge from the interaction of a multitude of simple individuals, is being conveniently modelled and simulated with cellular automata for very different purposes. In this book, a number of innovative applications of cellular automata models in the fields of Quantum Computing, Materials Science, Cryptography and Coding, and Robotics and Image Processing are presented