Dynamic Scheduling, Allocation, and Compaction Scheme for Real-Time Tasks on FPGAs

Run-time reconfiguration (RTR) is a method of computing on reconfigurable logic, typically FPGAs, changing hardware configurations from phase to phase of a computation at run-time. Recent research has expanded from a focus on a single application at a time to encompass a view of the reconfigurable logic as a resource shared among multiple applications or users. In real-time system design, task deadlines play an important role. Real-time multi-tasking systems not only need to support sharing of the resources in space, but also need to guarantee execution of the tasks. At the operating system level, sharing logic gates, wires, and I/O pins among multiple tasks needs to be managed. From the high level standpoint, access to the resources needs to be scheduled according to task deadlines. This thesis describes a task allocator for scheduling, placing, and compacting tasks on a shared FPGA under real-time constraints. Our consideration of task deadlines is novel in the setting of handling multiple simultaneous tasks in RTR. Software simulations have been conducted to evaluate the performance of the proposed scheme. The results indicate significant improvement by decreasing the number of tasks rejected

Geometric modeling for computer aided design

The primary goal of this grant has been the design and implementation of software to be used in the conceptual design of aerospace vehicles particularly focused on the elements of geometric design, graphical user interfaces, and the interaction of the multitude of software typically used in this engineering environment. This has resulted in the development of several analysis packages and design studies. These include two major software systems currently used in the conceptual level design of aerospace vehicles. These tools are SMART, the Solid Modeling Aerospace Research Tool, and EASIE, the Environment for Software Integration and Execution. Additional software tools were designed and implemented to address the needs of the engineer working in the conceptual design environment. SMART provides conceptual designers with a rapid prototyping capability and several engineering analysis capabilities. In addition, SMART has a carefully engineered user interface that makes it easy to learn and use. Finally, a number of specialty characteristics have been built into SMART which allow it to be used efficiently as a front end geometry processor for other analysis packages. EASIE provides a set of interactive utilities that simplify the task of building and executing computer aided design systems consisting of diverse, stand-alone, analysis codes. Resulting in a streamlining of the exchange of data between programs reducing errors and improving the efficiency. EASIE provides both a methodology and a collection of software tools to ease the task of coordinating engineering design and analysis codes

Integration of a big data emerging on large sparse simulation and its application on green computing platform

The process of analyzing large data and verifying a big data set are a challenge for understanding the fundamental concept behind it. Many big data analysis techniques suffer from the poor scalability, variation inequality, instability, lower convergence, and weak accuracy of the large-scale numerical algorithms. Due to these limitations, a wider opportunity for numerical analysts to develop the efficiency and novel parallel algorithms has emerged. Big data analytics plays an important role in the field of sciences and engineering for extracting patterns, trends, actionable information from large sets of data and improving strategies for making a decision. A large data set consists of a large-scale data collection via sensor network, transformation from signal to digital images, high resolution of a sensing system, industry forecasts, existing customer records to predict trends and prepare for new demand. This paper proposes three types of big data analytics in accordance to the analytics requirement involving a large-scale numerical simulation and mathematical modeling for solving a complex problem. First is a big data analytics for theory and fundamental of nanotechnology numerical simulation. Second, big data analytics for enhancing the digital images in 3D visualization, performance analysis of embedded system based on the large sparse data sets generated by the device. Lastly, extraction of patterns from the electroencephalogram (EEG) data set for detecting the horizontal-vertical eye movements. Thus, the process of examining a big data analytics is to investigate the behavior of hidden patterns, unknown correlations, identify anomalies, and discover structure inside unstructured data and extracting the essence, trend prediction, multi-dimensional visualization and real-time observation using the mathematical model. Parallel algorithms, mesh generation, domain-function decomposition approaches, inter-node communication design, mapping the subdomain, numerical analysis and parallel performance evaluations (PPE) are the processes of the big data analytics implementation. The superior of parallel numerical methods such as AGE, Brian and IADE were proven for solving a large sparse model on green computing by utilizing the obsolete computers, the old generation servers and outdated hardware, a distributed virtual memory and multi-processors. The integration of low-cost communication of message passing software and green computing platform is capable of increasing the PPE up to 60% when compared to the limited memory of a single processor. As a conclusion, large-scale numerical algorithms with great performance in scalability, equality, stability, convergence, and accuracy are important features in analyzing big data simulation

Interactive, Computation Assisted Design Tools

Realistic modeling, rendering, and animation of physical and virtual shapes have matured significantly over the last few decades. Yet, the creation and subsequent modeling of three-dimensional shapes remains a tedious task which requires not only artistic and creative talent, but also significant technical skill. The perfection witnessed in computer-generated feature films requires extensive manual processing and touch-ups. Every researcher working in graphics and related fields has likely experienced the difficulty of creating even a moderate-quality 3D model, whether based on a mental concept, a hand sketch, or inspirations from one or more photographs or existing 3D designs. This situation, frequently referred to as the content creation bottleneck, is arguably the major obstacle to making computer graphics as ubiquitous as it could be. Classical modeling techniques have primarily dealt with local or low-level geometric entities (e.g., points or triangles) and criteria (e.g., smoothness or detail preservation), lacking the freedom necessary to produce novel and creative content. A major unresolved challenge towards a new unhindered design paradigm is how to support the design process to create visually pleasing and yet functional objects by users who lack specialized skills and training. Most of the existing geometric modeling tools are intended either for use by experts (e.g., computer-aided design [CAD] systems) or for modeling objects whose visual aspects are the only consideration (e.g., computer graphics modeling systems). Furthermore, rapid prototyping, brought on by technological advances 3D printing has drastically altered production and consumption practices. These technologies empower individuals to design and produce original objects, customized according to their own needs. Thus, a new generation of design tools is needed to support both the creation of designs within the domain's constraints, that not only allows capturing the novice user's design intent but also meets the fabrication constraints such that the designs can be realized with minimal tweaking by experts. To fill this void, the premise of this thesis relies on the following two tenets: 1. users benefit from an interactive design environment that allows novice users to continuously explore a design space and immediately see the tradeoffs of their design choices. 2. the machine's processing power is used to assist and guide the user to maintain constraints imposed by the problem domain (e.g., fabrication/material constraints) as well as help the user in exploring feasible solutions close to their design intent. Finding the appropriate balance between interactive design tools and the computation needed for productive workflows is the problem addressed by this thesis. This thesis makes the following contributions: 1. We take a close look at thin shells--materials that have a thickness significantly smaller than other dimensions. Towards the goal of achieving interactive and controllable simulations we realize a particular geometric insight to develop an efficient bending model for the simulation of thin shells. Under isometric deformations (deformations that undergo little to no stretching), we can reduce the nonlinear bending energy into a cubic polynomial that has a linear Hessian. This linear Hessian can be further approximated with a constant one, providing significant speedups during simulation. We also build upon this simple bending model and show how orthotropic materials can be modeled and simulated efficiently. 2. We study the theory of Chebyshev nets--a geometric model of woven materials using a two-dimensional net composed of inextensible yarns. The theory of Chebyshev nets sheds some light on their limitations in globally covering a target surface. As it turns out, Chebyshev nets are a good geometric model for wire meshes, free-form surfaces composed of woven wires arranged in a regular grid. In the context of designing sculptures with wire mesh, we rely on the mathematical theory laid out by Hazzidakis~\cite{Hazzidakis1879} to determine an artistically driven workflow for approximately covering a target surface with a wire mesh, while globally maintaining material and fabrication constraints. This alleviates the user from worrying about feasibility and allows focus on design. 3. Finally, we present a practical design tool for the design and exploration of reconfigurables, defined as an object or collection of objects whose transformation between various states defines its functionality or aesthetic appeal (e.g., a mechanical assembly composed of interlocking pieces, a transforming folding bicycle, or a space-saving arrangement of apartment furniture). A novel space-time collision detection and response technique is presented that can be used to create an interactive workflow for managing and designing objects with various states. This work also considers a graph-based timeline during the design process instead of the traditional linear timeline and shows its many benefits as well as challenges for the design of reconfigurables

Integration of a big data emerging on large sparse simulation and its application on green computing platform

The process of analyzing large data and verifying a big data set are a challenge for understanding the fundamental concept behind it. Many big data analysis techniques suffer from the poor scalability, variation inequality, instability, lower convergence, and weak accuracy of the large-scale numerical algorithms. Due to these limitations, a wider opportunity for numerical analysts to develop the efficiency and novel parallel algorithms has emerged. Big data analytics plays an important role in the field of sciences and engineering for extracting patterns, trends, actionable information from large sets of data and improving strategies for making a decision. A large data set consists of a large-scale data collection via sensor network, transformation from signal to digital images, high resolution of a sensing system, industry forecasts, existing customer records to predict trends and prepare for new demand. This paper proposes three types of big data analytics in accordance to the analytics requirement involving a large-scale numerical simulation and mathematical modeling for solving a complex problem. First is a big data analytics for theory and fundamental of nanotechnology numerical simulation. Second, big data analytics for enhancing the digital images in 3D visualization, performance analysis of embedded system based on the large sparse data sets generated by the device. Lastly, extraction of patterns from the electroencephalogram (EEG) data set for detecting the horizontal-vertical eye movements. Thus, the process of examining a big data analytics is to investigate the behavior of hidden patterns, unknown correlations, identify anomalies, and discover structure inside unstructured data and extracting the essence, trend prediction, multi-dimensional visualization and real-time observation using the mathematical model. Parallel algorithms, mesh generation, domain-function decomposition approaches, inter-node communication design, mapping the subdomain, numerical analysis and parallel performance evaluations (PPE) are the processes of the big data analytics implementation. The superior of parallel numerical methods such as AGE, Brian and IADE were proven for solving a large sparse model on green computing by utilizing the obsolete computers, the old generation servers and outdated hardware, a distributed virtual memory and multi-processors. The integration of low-cost communication of message passing software and green computing platform is capable of increasing the PPE up to 60% when compared to the limited memory of a single processor. As a conclusion, large-scale numerical algorithms with great performance in scalability, equality, stability, convergence, and accuracy are important features in analyzing big data simulation

Packet Switched vs. Time Multiplexed FPGA Overlay Networks

Dedicated, spatially configured FPGA interconnect is efficient for applications that require high throughput connections between processing elements (PEs) but with a limited degree of PE interconnectivity (e.g. wiring up gates and datapaths). Applications which virtualize PEs may require a large number of distinct PE-to-PE connections (e.g. using one PE to simulate 100s of operators, each requiring input data from thousands of other operators), but with each connection having low throughput compared with the PE’s operating cycle time. In these highly interconnected conditions, dedicating spatial interconnect resources for all possible connections is costly and inefficient. Alternatively, we can time share physical network resources by virtualizing interconnect links, either by statically scheduling the sharing of resources prior to runtime or by dynamically negotiating resources at runtime. We explore the tradeoffs (e.g. area, route latency, route quality) between time-multiplexed and packet-switched networks overlayed on top of commodity FPGAs. We demonstrate modular and scalable networks which operate on a Xilinx XC2V6000-4 at 166MHz. For our applications, time-multiplexed, offline scheduling offers up to a 63% performance increase over online, packet-switched scheduling for equivalent topologies. When applying designs to equivalent area, packet-switching is up to 2× faster for small area designs while time-multiplexing is up to 5× faster for larger area designs. When limited to the capacity of a XC2V6000, if all communication is known, time-multiplexed routing outperforms packet-switching; however when the active set of links drops below 40% of the potential links, packet-switched routing can outperform time-multiplexing

Sublogarithmic deterministic selection on arrays with a reconfigurable optical bus

The linear array with a reconfigurable pipelined bus system (LARPBS) is a newly introduced parallel computational model, where processors are connected by a reconfigurable optical bus. In this paper, we show that the selection problem can be solved on the LARPBS model deterministically in O((loglogN)2/ log log log N) time. To our best knowledge, this is the best deterministic selection algorithm on any model with a reconfigurable optical bus.Yijie Han, Yi Pan and Hong She

Exploiting the HTX-Board as a Coprocessor for Exact Arithmetics

Certain numerical computations benefit from dedicated computation units, e.g. providing increased computation accuracy. Exploiting current interconnection technologies and advances in reconfigurable logic, restrictions and drawbacks of past approaches towards application-specific units can be overcome. This paper presents our implementation of an FPGA-based hardware unit for exact arithmetics. The unit is tightly integrated into the host system using state-of-the-art HyperTransport technology. An according runtime system provides OS-level support including dynamic function resolution. The approach demonstrates suitability and applicability of the chosen technologies, setting the pace towards broadly acceptable use of reconfigurable coprocessor technology for application-specific computing