Content addressable memory project

A parameterized version of the tree processor was designed and tested (by simulation). The leaf processor design is 90 percent complete. We expect to complete and test a combination of tree and leaf cell designs in the next period. Work is proceeding on algorithms for the computer aided manufacturing (CAM), and once the design is complete we will begin simulating algorithms for large problems. The following topics are covered: (1) the practical implementation of content addressable memory; (2) design of a LEAF cell for the Rutgers CAM architecture; (3) a circuit design tool user's manual; and (4) design and analysis of efficient hierarchical interconnection networks

STRICT: a language and tool set for the design of very large scale integrated circuits

PhD ThesisAn essential requirement for the design of large VLSI circuits is a design methodology which would allow the designer to overcome the complexity and correctness issues associated with the building of such circuits. We propose that many of the problems of the design of large circuits can be solved by using a formal design notation based upon the functional programming paradigm, that embodies design concepts that have been used extensively as the framework for software construction. The design notation should permit parallel, sequential, and recursive decompositions of a design into smaller components, and it should allow large circuits to be constructed from simpler circuits that can be embedded in a design in a modular fashion. Consistency checking should be provided as early as possible in a design. Such a methodology would structure the design of a circuit in much the same way that procedures, classes, and control structures may be used to structure large software systems. However, such a design notation must be supported by tools which automatically check the consistency of the design, if the methodology is to be practical. In principle, the methodology should impose constraints upon circuit design to reduce errors and provide' correctness by construction' . It should be possible to generate efficient and correct circuits, by providing a route to a large variety of design tools commonly found in design systems: simulators, automatic placement and routing tools, module generators, schematic capture tools, and formal verification and synthesis tools

High Level Synthesis of Neural Network Chips

This thesis investigates the development of a silicon compiler dedicated to generate Application-Specific Neural Network Chips (ASNNCs) from a high level C-based behavioural specification language. The aim is to fully integrate the silicon compiler with the ESPRIT II Pygmalion neural programming environment. The integration of these two tools permits the translation of a neural network application specified in nC, the Pygmalion's C-based neural programming language, into either binary (for simulation) or silicon (for execution in hardware). Several applications benefit from this approach, in particular the ones that require real-time execution, for which a true neural computer is required. This research comprises two major parts: extension of the Pygmalion neural programming environment, to support automatic generation of neural network chips from the nC specification language; and implementation of the high level synthesis part of the neural silicon compiler. The extension of the neural programming environment has been developed to adapt the nC language to hardware constraints, and to provide the environment with a simulation tool to test in advance the performance of the neural chips. Firstly, new hardware-specific requisites have been incorporated to nC. However, special attention has been taken to avoid transforming nC into a hardware-oriented language, since the system assumes minimum (or even no) knowledge of VLSI design from the application developer. Secondly, a simulator for neural network hardware has been developed, which assesses how well the generated circuit will perform the neural computation. Lastly, a hardware library of neural network models associated with a target VLSI architecture has been built. The development of the neural silicon compiler focuses on the high level synthesis part of the process. The goal of the silicon compiler is to take nC as the input language and automatically translate it into one or more identical integrated circuits, which are specified in VHDL (the IEEE standard hardware description language) at the register transfer level. The development of the high level synthesis comprises four major parts: firstly, compilation and software-like optimisations of nC; secondly, transformation of the compiled code into a graph-based internal representation, which has been designed to be the basis for the hardware synthesis; thirdly, further transformations and hardware-like optimisations on the internal representation; and finally, creation of the neural chip's data path and control unit that implement the behaviour specified in nC. Special attention has been devoted to the creation of optimised hardware structures for the ASNNCs employing both phases of neural computing on-chip: recall and learning. This is achieved through the data path and control synthesis algorithms, which adopt a heuristic approach that targets the generated hardware structure of the neural chip in a specific VLSI architecture, namely the Generic Neuron. The viability, concerning the effective use of silicon area versus speed, has been evaluated through the automatic generation of a VHDL description for the neural chip employing the Back Propagation neural network model. This description is compared with the one created manually by a hardware designer

A hierarchical distributed control model for coordinating intelligent systems

A hierarchical distributed control (HDC) model for coordinating cooperative problem-solving among intelligent systems is described. The model was implemented using SOCIAL, an innovative object-oriented tool for integrating heterogeneous, distributed software systems. SOCIAL embeds applications in 'wrapper' objects called Agents, which supply predefined capabilities for distributed communication, control, data specification, and translation. The HDC model is realized in SOCIAL as a 'Manager'Agent that coordinates interactions among application Agents. The HDC Manager: indexes the capabilities of application Agents; routes request messages to suitable server Agents; and stores results in a commonly accessible 'Bulletin-Board'. This centralized control model is illustrated in a fault diagnosis application for launch operations support of the Space Shuttle fleet at NASA, Kennedy Space Center

Spartan Daily, February 15, 1957

Volume 44, Issue 72https://scholarworks.sjsu.edu/spartandaily/12428/thumbnail.jp

Modeling and Simulation of a Hard Real-Time Processor

Hard real-time systems are increasingly used in various areas of human activity justifying their implementation by means of specialized solutions, mostly of a suitable hardware/software combination. A frequently adopted approach to the realization of the hardware part is based on ASIC, usually a “general purpose” processor which operates according to real-time constraints. In this work the modeling of a processor for the hard real-time domain is described. It is structured as a collection of “task processors” being supervised by another one dedicated to the “kernel” functions. Specifically the behavior of the task processors is modeled using VHDL and subsequently simulated and tested. The paper also addresses the modeling process by determining the detailed requirements on the behavior of the task processor. The outcome of this step influenced the modeling process as the tools used were of restricted functionality and processor behavior enforced a particular decomposition. Because of a restricted VHDL subset available, it was necessary to model the task processor on the lowest level of behavioral abstraction. The task processor has been tested against chosen test programs written in an appropriate assembly language being specially developed for this purpose