Hypersonic Research Vehicle (HRV) real-time flight test support feasibility and requirements study. Part 2: Remote computation support for flight systems functions

The requirements are assessed for the use of remote computation to support HRV flight testing. First, remote computational requirements were developed to support functions that will eventually be performed onboard operational vehicles of this type. These functions which either cannot be performed onboard in the time frame of initial HRV flight test programs because the technology of airborne computers will not be sufficiently advanced to support the computational loads required, or it is not desirable to perform the functions onboard in the flight test program for other reasons. Second, remote computational support either required or highly desirable to conduct flight testing itself was addressed. The use is proposed of an Automated Flight Management System which is described in conceptual detail. Third, autonomous operations is discussed and finally, unmanned operations

The Application Of RISC Processors To Training Simulators

Report on a study of the utility of reduced instruction set computer processors as the control computers in a training simulator. Report includes a master\u27s thesis on detailed hardware design for interfacing transputer hardware to the NeXT computer

Parallel process placement

This thesis investigates methods of automatic allocation of processes to available processors in a given network configuration. The research described covers the investigation of various algorithms for optimal process allocation. Among those researched were an algorithm which used a branch and bound technique, an algorithm based on graph theory, and an heuristic algorithm involving cluster analysis. These have been implemented and tested in conjunction with the gathering of performance statistics during program execution, for use in improving subsequent allocations. The system has been implemented on a network of loosely-coupled microcomputers using multi-port serial communication links to simulate a transputer network. The concurrent programming language occam has been implemented, replacing the explicit process allocation constructs with an automatic placement algorithm. This enables the source code to be completely separated from hardware consideration

Aplikasi Jaringan Syaraf Tiruan Berbasis Transputer Pada Simulasi Sistem Drive-RIG

Transputer merupakan mikroprosesor yang telah berisi prosesor, memory lokal dan serial link kecepatan tinggi untuk komunikasi antar transputer pada sistem komputasi paralel. Untuk mengeksploitasi aplikasi transputer pada sistem-sistem konkuren dapat didesain modul-modul proses secara konkuren melalui link. Salah satu kendala yang mcndasar dalam merancang sistem jaringan syaraf tiruan (JST) adalah sifat sekuensial dari komputer konvensional, yang bertentangan dengan sifat bawaan yang dimililci oleh JST, yaitu pemrosesan secara paralel. Dalam tugas akhir ini dibahas parallel processing JST dengan aplikasi pada plant dinamis sebagai kontrolcr sistem electric drive-rig. Dengan mendistribusikan beberapa proses secara konkuren diharapkan akan menambah unjuk kerja sistem dalam tracking plant dinamis

The instruction of systolic array (ISA) and simulation of parallel algorithms

Systolic arrays have proved to be well suited for Very Large Scale Integrated technology (VLSI) since they: -Consist of a regular network of simple processing cells, -Use local communication between the processing cells only, -Exploit a maximal degree of parallelism. However, systolic arrays have one main disadvantage compared with other parallel computer architectures: they are special purpose architectures only capable of executing one algorithm, e.g., a systolic array designed for sorting cannot be used to form matrix multiplication. Several approaches have been made to make systolic arrays more flexible, in order to be able to handle different problems on a single systolic array. In this thesis an alternative concept to a VLSI-architecture the Soft-Systolic Simulation System (SSSS), is introduced and developed as a working model of virtual machine with the power to simulate hard systolic arrays and more general forms of concurrency such as the SIMD and MIMD models of computation. The virtual machine includes a processing element consisting of a soft-systolic processor implemented in the virtual.machine language. The processing element considered here was a very general element which allows the choice of a wide range of arithmetic and logical operators and allows the simulation of a wide class of algorithms but in principle extra processing cells can be added making a library and this library be tailored to individual needs. The virtual machine chosen for this implementation is the Instruction Systolic Array (ISA). The ISA has a number of interesting features, firstly it has been used to simulate all SIMD algorithms and many MIMD algorithms by a simple program transformation technique, further, the ISA can also simulate the so-called wavefront processor algorithms, as well as many hard systolic algorithms. The ISA removes the need for the broadcasting of data which is a feature of SIMD algorithms (limiting the size of the machine and its cycle time) and also presents a fairly simple communication structure for MIMD algorithms. The model of systolic computation developed from the VLSI approach to systolic arrays is such that the processing surface is fixed, as are the processing elements or cells by virtue of their being embedded in the processing surface. The VLSI approach therefore freezes instructions and hardware relative to the movement of data with the virtual machine and softsystolic programming retaining the constructions of VLSI for array design features such as regularity, simplicity and local communication, allowing the movement of instructions with respect to data. Data can be frozen into the structure with instructions moving systolically. Alternatively both the data and instructions can move systolically around the virtual processors, (which are deemed fixed relative to the underlying architecture). The ISA is implemented in OCCAM programs whose execution and output implicitly confirm the correctness of the design. The soft-systolic preparation comprises of the usual operating system facilities for the creation and modification of files during the development of new programs and ISA processor elements. We allow any concurrent high level language to be used to model the softsystolic program. Consequently the Replicating Instruction Systolic Array Language (RI SAL) was devised to provide a very primitive program environment to the ISA but adequate for testing. RI SAL accepts instructions in an assembler-like form, but is fairly permissive about the format of statements, subject of course to syntax. The RI SAL compiler is adopted to transform the soft-systolic program description (RISAL) into a form suitable for the virtual machine (simulating the algorithm) to run. Finally we conclude that the principles mentioned here can form the basis for a soft-systolic simulator using an orthogonally connected mesh of processors. The wide range of algorithms which the ISA can simulate make it suitable for a virtual simulating grid

An instruction systolic array architecture for multiple neural network types

Modern electronic systems, especially sensor and imaging systems, are beginning to incorporate their own neural network subsystems. In order for these neural systems to learn in real-time they must be implemented using VLSI technology, with as much of the learning processes incorporated on-chip as is possible. The majority of current VLSI implementations literally implement a series of neural processing cells, which can be connected together in an arbitrary fashion. Many do not perform the entire neural learning process on-chip, instead relying on other external systems to carry out part of the computation requirements of the algorithm. The work presented here utilises two dimensional instruction systolic arrays in an attempt to define a general neural architecture which is closer to the biological basis of neural networks - it is the synapses themselves, rather than the neurons, that have dedicated processing units. A unified architecture is described which can be programmed at the microcode level in order to facilitate the processing of multiple neural network types. An essential part of neural network processing is the neuron activation function, which can range from a sequential algorithm to a discrete mathematical expression. The architecture presented can easily carry out the sequential functions, and introduces a fast method of mathematical approximation for the more complex functions. This can be evaluated on-chip, thus implementing the entire neural process within a single system. VHDL circuit descriptions for the chip have been generated, and the systolic processing algorithms and associated microcode instruction set for three different neural paradigms have been designed. A software simulator of the architecture has been written, giving results for several common applications in the field

LAPSES: A Recipe for High-Performance Adaptive Router Design

Earlier research has shown that adaptive routing can help in improving network performance. However, it has not received adequate attention in commercial routers mainly due to the additional hardware complexity, and the perceived cost and performance degradation that may result from this complexity. These concerns can be mitigated if one can design a cost-effective router that can support adaptive routing. This paper proposes a three step recipe — Look-Ahead routing, intelligent Path Selection, and an Economic Storage implementation, called the LAPSES approach — for cost-effective high performance pipelined adaptive router design. The first step, look-ahead routing, reduces a pipeline stage in the router by making table lookup and arbitration concurrent. Next, three new traffic-sensitive path selection heuristics (LRU, LFU and MAX-CREDIT) are proposed to select one of the available alternate paths. Finally, two techniques for reducing routing table size of the adaptive router are presented. These are called meta-table routing and economical storage. The proposed economical storage needs a routing table with only 9 and 27 entries for two and three dimensional meshes, respectively. All these design ideas are evaluated on a (16 16) mesh network via simulation. A fully adaptive algorithm and various traffic patterns are used to examine the performance benefits. Performance results show that the look-ahead design as well as the path selection heuristics boost network performance, while the economical storage approach turns out to be an ideal choice in comparison to full-table and meta-table options. We believe the router resulting from these three design enhancements can make adaptive routing a viable choice for interconnects.