The finite element machine: An experiment in parallel processing

The finite element machine is a prototype computer designed to support parallel solutions to structural analysis problems. The hardware architecture and support software for the machine, initial solution algorithms and test applications, and preliminary results are described

Design and resource management of reconfigurable multiprocessors for data-parallel applications

FPGA (Field-Programmable Gate Array)-based custom reconfigurable computing machines have established themselves as low-cost and low-risk alternatives to ASIC (Application-Specific Integrated Circuit) implementations and general-purpose microprocessors in accelerating a wide range of computation-intensive applications. Most often they are Application Specific Programmable Circuiits (ASPCs), which are developer programmable instead of user programmable. The major disadvantages of ASPCs are minimal programmability, and significant time and energy overheads caused by required hardware reconfiguration when the problem size outnumbers the available reconfigurable resources; these problems are expected to become more serious with increases in the FPGA chip size. On the other hand, dominant high-performance computing systems, such as PC clusters and SMPs (Symmetric Multiprocessors), suffer from high communication latencies and/or scalability problems. This research introduces low-cost, user-programmable and reconfigurable MultiProcessor-on-a-Programmable-Chip (MPoPC) systems for high-performance, low-cost computing. It also proposes a relevant resource management framework that deals with performance, power consumption and energy issues. These semi-customized systems reduce significantly runtime device reconfiguration by employing userprogrammable processing elements that are reusable for different tasks in large, complex applications. For the sake of illustration, two different types of MPoPCs with hardware FPUs (floating-point units) are designed and implemented for credible performance evaluation and modeling: the coarse-grain MIMD (Multiple-Instruction, Multiple-Data) CG-MPoPC machine based on a processor IP (Intellectual Property) core and the mixed-mode (MIMD, SIMD or M-SIMD) variant-grain HERA (HEterogeneous Reconfigurable Architecture) machine. In addition to alleviating the above difficulties, MPoPCs can offer several performance and energy advantages to our data-parallel applications when compared to ASPCs; they are simpler and more scalable, and have less verification time and cost. Various common computation-intensive benchmark algorithms, such as matrix-matrix multiplication (MMM) and LU factorization, are studied and their parallel solutions are shown for the two MPoPCs. The performance is evaluated with large sparse real-world matrices primarily from power engineering. We expect even further performance gains on MPoPCs in the near future by employing ever improving FPGAs. The innovative nature of this work has the potential to guide research in this arising field of high-performance, low-cost reconfigurable computing. The largest advantage of reconfigurable logic lies in its large degree of hardware customization and reconfiguration which allows reusing the resources to match the computation and communication needs of applications. Therefore, a major effort in the presented design methodology for mixed-mode MPoPCs, like HERA, is devoted to effective resource management. A two-phase approach is applied. A mixed-mode weighted Task Flow Graph (w-TFG) is first constructed for any given application, where tasks are classified according to their most appropriate computing mode (e.g., SIMD or MIMD). At compile time, an architecture is customized and synthesized for the TFG using an Integer Linear Programming (ILP) formulation and a parameterized hardware component library. Various run-time scheduling schemes with different performanceenergy objectives are proposed. A system-level energy model for HERA, which is based on low-level implementation data and run-time statistics, is proposed to guide performance-energy trade-off decisions. A parallel power flow analysis technique based on Newton\u27s method is proposed and employed to verify the methodology

Computation Enhancement using Reconfigurable Computing

In light of the industry’s constant need for better computer performance, this project aims to choose and evaluate an approach for facing this issue. The targeted category of computers is single board computers (e.g. Raspberry Pi). The approach utilized for enhancing performance is the use of reconfigurable computing as to execute computationally expensive calculations on a runtime custom-tailored hardware. The objective of this project is the test of the potential this approach has for increasing computers performance through comparing a software implementation of an algorithm with an FPGA assisted implementation of the same algorithm. The platforms chosen for this project are the Rapsberry Pi and the Parallella P1602 board with its Zynq SoC for the software implementation and the FPGA assisted implementation in that order. The chosen algorithm is Fourier Fast Transform due to its part in many DSP applications and its suitability for the project objective. While the software solution worked successfully resulting in an asymptotic cost of O(N log N); the reconfigurable computing solution couldn’t be completed due to time constraints and lack of experience of the student. Future work should complete the experiment and add a multicore implementation of the same algorithm to add yet another class to the comparison

An Implementation of Membrane Computing Using Reconfigurable Hardware

Because of their inherent large-scale parallelism, membrane computing models can be fully exploited only through the use of a parallel computing platform. We have fully implemented such a computing platform based on reconfigurable hardware that is intended to support the efficient execution of membrane computing models. This computing platform is the first of its type to implement parallelism at both the system and region levels. In this paper, we describe how our computing platform implements the core features of membrane computing models in hardware, and present a theoretical performance analysis of the algorithm it executes in hardware. The performance analysis suggests that the computing platform can significantly outperform sequential implementations of membrane computing as well as Petreska and Teuscher's hardware implementation, the only other complete hardware implementation of membrane computing in existence

Development of a Reconfigurable Multi-Faceted Communications Device Using Partial Dynamic Reconfiguration

Supporting a variety of communication protocols has typically required extensive hard- ware and Input/Output (I/O) interfaces targeting each protocol specifically. Recent designs in the past ten years have created more dynamic approaches by using Field Programmable Gate Arrays (FPGAs) and embedded hardware to implement or simulate previous hardware I/O designs. With the constant increase in FPGA and embedded technologies the capabilities of dynamic implementations have expanded. This report addresses the design of an up-to-date reconfigurable multi-faceted embedded device targeting recent technological advances in FPGAs

Fusion and Perspective Correction of Multiple Networked Video Sensors

A network of adaptive processing elements has been developed that transforms and fuses video captured from multiple sensors. Unlike systems that rely on end-systems to process data, this system distributes the computation throughout the network in order to reduce overall network bandwidth. The network architecture is scalable because it uses a hierarchy of processing engines to perform signal processing. Nodes within the network can be dynamically reprogrammed in order to compose video from multiple sources, digitally transform camera perspectives, and adapt the video format to meet the needs of specific applications. A prototype has been developed using reconfigurable hardware that collects and processes real-time, streaming video of an urban environment. Multiple video cameras gather data from different perspectives and fuse that data into a unified, top-down view. The hardware exploits both the spatial and temporal parallelism of the video streams and the regular processing when applying the transforms. Recon-figurable hardware allows for the functions at nodes to be reprogrammed for dynamic changes in topology. Hardware-based video processors also consume less power than high frequency software-based solutions. Performance and scalability are compared to a distributed software-based implementation. The reconfigurable hardware design is coded in VHDL and prototyped using Washington University’s Field Programmable Port Extender (FPX) platform. The transform engine circuit utilizes approximately 34 percent of the resources of a Xilinx Virtex 2000E FPGA, and can be clocked at frequencies up to 48 MHz. The com-position engine circuit utilizes approximately 39 percent of the resources of a Xilinx Virtex 2000E FPGA, and can be clocked at frequencies up to 45 MHz

New techniques for functional testing of microprocessor based systems

Electronic devices may be affected by failures, for example due to physical defects. These defects may be introduced during the manufacturing process, as well as during the normal operating life of the device due to aging. How to detect all these defects is not a trivial task, especially in complex systems such as processor cores. Nevertheless, safety-critical applications do not tolerate failures, this is the reason why testing such devices is needed so to guarantee a correct behavior at any time. Moreover, testing is a key parameter for assessing the quality of a manufactured product. Consolidated testing techniques are based on special Design for Testability (DfT) features added in the original design to facilitate test effectiveness. Design, integration, and usage of the available DfT for testing purposes are fully supported by commercial EDA tools, hence approaches based on DfT are the standard solutions adopted by silicon vendors for testing their devices. Tests exploiting the available DfT such as scan-chains manipulate the internal state of the system, differently to the normal functional mode, passing through unreachable configurations. Alternative solutions that do not violate such functional mode are defined as functional tests. In microprocessor based systems, functional testing techniques include software-based self-test (SBST), i.e., a piece of software (referred to as test program) which is uploaded in the system available memory and executed, with the purpose of exciting a specific part of the system and observing the effects of possible defects affecting it. SBST has been widely-studies by the research community for years, but its adoption by the industry is quite recent. My research activities have been mainly focused on the industrial perspective of SBST. The problem of providing an effective development flow and guidelines for integrating SBST in the available operating systems have been tackled and results have been provided on microprocessor based systems for the automotive domain. Remarkably, new algorithms have been also introduced with respect to state-of-the-art approaches, which can be systematically implemented to enrich SBST suites of test programs for modern microprocessor based systems. The proposed development flow and algorithms are being currently employed in real electronic control units for automotive products. Moreover, a special hardware infrastructure purposely embedded in modern devices for interconnecting the numerous on-board instruments has been interest of my research as well. This solution is known as reconfigurable scan networks (RSNs) and its practical adoption is growing fast as new standards have been created. Test and diagnosis methodologies have been proposed targeting specific RSN features, aimed at checking whether the reconfigurability of such networks has not been corrupted by defects and, in this case, at identifying the defective elements of the network. The contribution of my work in this field has also been included in the first suite of public-domain benchmark networks

Solution of partial differential equations on vector and parallel computers

The present status of numerical methods for partial differential equations on vector and parallel computers was reviewed. The relevant aspects of these computers are discussed and a brief review of their development is included, with particular attention paid to those characteristics that influence algorithm selection. Both direct and iterative methods are given for elliptic equations as well as explicit and implicit methods for initial boundary value problems. The intent is to point out attractive methods as well as areas where this class of computer architecture cannot be fully utilized because of either hardware restrictions or the lack of adequate algorithms. Application areas utilizing these computers are briefly discussed