The STAR-C Truth: Analyzing Reconfigurable Supercomputing Reliability

This book covers technologies, applications, tools, languages, procedures, advantages, and disadvantages of reconfigurable supercomputing using Field Programmable Gate Arrays (FPGAs). The target audience is the community of users of High Performance Computers (HPC) who may benefit from porting their applications into a reconfigurable environment. As such, this book is intended to guide the HPC user through the many algorithmic considerations, hardware alternatives, usability issues, programming languages, and design tools that need to be understood before embarking on the creation of reconfigurable parallel codes. We hope to show that FPGA acceleration, based on the exploitation of the data parallelism, pipelining and concurrency remains promising in view of the diminishing improvements in traditional processor and system design. Table of Contents: FPGA Technology / Reconfigurable Supercomputing / Algorithmic Considerations / FPGA Programming Languages / Case Study: Sorting / Alternative Technologies and Concluding Remarks

Chapter 5 - FPGA Device-Level Design Decisions

This thesis work presents the implementation and validation of image processing problems in hardware to estimate the performance and precision gain. It compares the implementation for the addressed problem on a Field Programmable Gate Array (FPGA) with a software implementation for a General Purpose Processor (GPP) architecture. For both solutions the implementation costs for their development is an important aspect in the validation. The analysis of the flexibility and extendability that can be achieved by a modular implementation for the FPGA design was another major aspect. This work is based upon approaches from previous work, which included the detection of Binary Large OBjects (BLOBs) in static images and continuous video streams [13, 15]. One addressed problem of this work is the tracking of the detected BLOBs in continuous image material. This has been implemented for the FPGA platform and the GPP architecture. Both approaches have been compared with respect to performance and precision. This research project is motivated by the MI6 project of the Computer Vision research group, which is located at the Bonn-Rhein-Sieg University of Applied Sciences. The intent of the MI6 project is the tracking of a user in an immersive environment. The proposed solution is to attach a light emitting device to the user for tracking the created light dots on the projection surface of the immersive environment. Having the center points of those light dots would allow the estimation of the user’s position and orientation. One major issue that makes Computer Vision problems computationally expensive is the high amount of data that has to be processed in real-time. Therefore, one major target for the implementation was to get a processing speed of more than 30 frames per second. This would allow the system to realize feedback to the user in a response time which is faster than the human visual perception. One problem that comes with the idea of using a light emitting device to represent the user, is the precision error. Dependent on the resolution of the tracked projection surface of the immersive environment, a pixel might have a size in cm2. Having a precision error of only a few pixels, might lead to an offset in the estimated user’s position of several cm. In this research work the development and validation of a detection and tracking system for BLOBs on a Cyclone II FPGA from Altera has been realized. The system supports different input devices for the image acquisition and can perform detection and tracking for five to eight BLOBs. A further extension of the design has been evaluated and is possible with some constraints. Additional modules for compressing the image data based on run-length encoding and sub-pixel precision for the computed BLOB center-points have been designed. For the comparison of the FPGA approach for BLOB tracking a similar implementation in software using a multi-threaded approach has been realized. The system can transmit the detection or tracking results on two available communication interfaces, USB and RS232. The analysis of the hardware solution showed a similar precision for the BLOB detection and tracking as the software approach. One problem is the strong increase of the allocated resources when extending the system to process more BLOBs. With one of the applied target platforms, the DE2-70 board from Altera, the BLOB detection could be extended to process up to thirty BLOBs. The implementation of the tracking approach in hardware required much more effort than the software solution. The design of high level problems in hardware for this case are more expensive than the software implementation. The search and match steps in the tracking approach could be realized more efficiently and reliably in software. The additional pre-processing modules for sub-pixel precision and run-length-encoding helped to increase the system’s performance and precision.

Chapter 6 - Board-Level Design Decisions and Allocation

Hyperspectral imagery is a class of image data which is used in many scientific areas, most notably, medical imaging and remote sensing. It is characterized by a wealth of spatial and spectral information. Over the last years, many algorithms have been developed with the purpose of finding "spectral endmembers," which are assumed to be pure signatures in remotely sensed hyperspectral data sets. Such pure signatures can then be used to estimate the abundance or concentration of materials in mixed pixels, thus allowing sub-pixel analysis which is crucial in many remote sensing applications due to current sensor optics and configuration. One of the most popular endmember extraction algorithms has been the pixel purity index (PPI), available from Kodak's Research Systems ENVI software package. This algorithm is very time consuming, a fact that has generally prevented its exploitation in valid response times in a wide range of applications, including environmental monitoring, military applications or hazard and threat assessment/tracking (including wildland fire detection, oil spill mapping and chemical and biological standoff detection). Field programmable gate arrays (FPGAs) are hardware components with millions of gates. Their reprogrammability and high computational power makes them particularly attractive in remote sensing applications which require a response in near real-time. In this paper, we present an FPGA design for implementation of PPI algorithm which takes advantage of a recently developed fast PPI (FPPI) algorithm that relies on software-based optimization. The proposed FPGA design represents our first step toward the development of a new reconfigurable system for fast, onboard analysis of remotely sensed hyperspectral imagery.

Ternary Content Addressable Memory (TCAM) is widely used in network infrastructure for various search functions. There has been a growing interest in implementing TCAM using reconfigurable hardware such as Field Programmable Gate Array (FPGA). Most of existing FPGA-based TCAM designs are based on brute-force implementations, which result in inefficient on-chip resource usage. As a result, existing designs support only a small TCAM size even with large FPGA devices. They also suffer from significant throughput degradation in implementing a large TCAM, mainly caused by deep priority encoding. This paper presents a scalable random access memory (RAM)-based TCAM architecture aiming for efficient implementation on state-of-the-art FPGAs. We give a formal study on RAM-based TCAM to unveil the ideas and the algorithms behind it. To conquer the timing challenge, we propose a modular architecture consisting of arrays of small-size RAM-based TCAM units. After decoupling the update logic from each unit, the modular architecture allows us to share each update engine among multiple units. This leads to resource saving. The capability of explicit range matching is also offered to avoid range-to-ternary conversion for search functions that require range matching. Implementation on a Xilinx Virtex 7 FPGA shows that our design can support a large TCAM of up to 2.4 Mbits while sustaining high throughput of 150 million packets per second. The resource usage scales linearly with the TCAM size. The architecture is configurable, allowing various performance trade-offs to be exploited. To the best of our knowledge, this is the first FPGA design that implements a TCAM larger than 1 Mbits.

Field Programmable Gate Arrays (FPGAs) have gained popularity because their reconfigurability can speed up development and verification with relatively low cost. However the deep level of understanding required on hardware logic programming has discouraged many software engineers. An interface between host devices and FPGAs to enable designing and programming FPGAs using a software programming standard and encapsulating hardware details is much desired. In this paper we evaluate leveraging Open Computing Language (OpenCL) to rapidly design FPGAs, considering both hardware logic utilization efficiency and computing performance. On a heterogeneous computer system consisting of ARM processors and Altera FPGA, we execute an OpenCL host program on the ARM processors and an OpenCL kernel on the FPGA, to compute a parametrizable two-dimensional Mandelbrot fractal. We explore three design aspects of adjusting OpenCL work-group size, coalescing memory access, and replicating compute units to improve the FPGA computation performance. After optimizing the core algorithm, we efficiently reduced the logic utilization and Digital Signal Processing (DSP) blocks required for a single compute unit, and successfully increased the number of replicated compute units from four to six, thus delivering a 1.5X increase of parallel computation capacity of the FPGA, and improving the computing speed by 1.5X and memory bandwidth by 1.7X.

A high-level Field Programmable Gate Array (FPGA) prototype for a JPEG image encoder has been developed by the Structures, Pointing, and Control Engineering (SPACE) University Research Center (URC). The FPGA prototype uses MathWorks Simulink and Xilinx System Generator for deployment on its Virtex-5 FPGA board. The FPGA module serves as a co-processor of a real-time Ubiquitous Video Conferencing (UVC) software package. The project objective is to: 1) study the high-level synthesis method for FPGA design, and 2) provide the digital signal processing necessary to offload computationally-intensive video processing data from a host computer running the UVC application [1]. The project uses a Xilinx XUPV5-LV110T FPGA Development System. The image compression utilizes the JPEG standard. Each module for the JPEG encoder is designed in Simulink. To reduce computation time on the FPGA due to floating-point Discrete Cosine Transform (DCT) calculations, a table containing DCT cosine product values was computed in Excel, accurate to two decimal digits. These values are shifted 6 bits to the left to maintain accuracy and loaded onto the FPGA. The product of 8-bit YCbCr channel data and tabulated values is accumulated into a register, then scaled and shifted 6 bits to the right to complete the DCT.

The CMOS technology scaling has greatly improved the overall performance and density of Field Programmable Gate Arrays (FPGAs). However, when looking at the performance metrics such as speed, area and power consumption, the gap is generally very wide for FPGAs compared to application specific integrated circuits (ASICs) mainly due to the programmable interconnect overhead. We propose a 3-dimensional (3D) design methodology using horizontal design partitioning to vertically stack heterogeneous FPGA designs based on a Tree-based multilevel FPGA architecture. We describe the 3D design and optimization methodology to improve speed, interconnect area and power consumption using Tezzaron's 3D stacking technology.

In this paper, we investigate how the presence of a general purpose operating system influences the reliability of modern embedded Systems-on-Chips (SoCs). We experimentally study the difference in the neutron-induced error rate of SoCs when executing the application bare to the metal (i.e., without an underlying operating system) and on top of the Linux kernel. Our analysis demonstrates that Linux presence barely affects the Silent Data Corruption (SDC) rate whereas it greatly increases the system Functional Interruption (SEFI) rate (up to 7.48 times) if no preventive measures are taken. Nevertheless, we experimentally demonstrate that cache conflicts between the operating system and application can be leveraged to significantly reduce the Linux-induced SEFI rate increase. Moreover, we evaluate the OS software stack masking effect and show that the higher the abstraction layer in which an application is implemented, the lower its SDC rate. Furthermore, we analyze system reliability taking into account not only the resulting failure rates, but also the execution (and, thus, exposure) times.

Background: In recent years, the rising demand for high-performance radar systems in aeronautics, astronautics, national defense, and civil communication has driven the widespread application of phased-array technology. The design of beam controllers, the core of phased-array systems, faces many challenges. Traditional implementation schemes are usually based on application-specific integrated circuits (ASIC) or discrete components, which have a long development cycle, poor flexibility, and limited adaptability. Field-programmable gate arrays (FPGA), with their high parallelism, reconfigurability, and powerful signal processing capabilities, offer a new way to implement phased-array control systems. But FPGA design has traditionally relied on register transfer level (RTL) description, which is inefficient and struggles to keep up with the rapid iteration of complex algorithms. With the development of high-level synthesis (HLS) technology, it has become possible to develop FPGAs using high-level languages such as C/C++. However, improving development efficiency while ensuring hardware performance remains a key issue in the design of phased-array beam controllers. Materials and Methods: This study employs an HLS - RTL co - design method to build a phased - array beam controller. This approach combines the strengths of HLS's efficient algorithm implementation and RTL's precise hardware interface control. The system architecture consists of three parts: the host - machine control layer, FPGA processing layer, and phase - control output layer. In the FPGA processing layer, the core beam - forming algorithm is implemented via HLS. Based on phased - array principles, this algorithm converts target angles to wave vectors and calculates phase - compensation values for each element in an 8×8 array. Pipelining and parallel processing techniques are used in the HLS design to optimize algorithm throughput and latency. The phase - control output layer, designed with RTL, repackages 64 calculated phase values and distributes them to 16 physical phase - shifter chips. Each phase - shifter manages four elements and receives control data through an SPI interface. Results: The proposed FPGA-based phased-array beam controller design in this paper has successfully achieved high - speed, high - precision beam control. By using the HLS - RTL co - design method, it has solved the problem of low algorithm implementation efficiency in traditional FPGA development and ensured the high performance and reliability of hardware interfaces. Experimental results indicate that the system meets or surpasses design targets on key performance indicators. Its end - to - end latency is kept within 130μs, it supports an angle update rate of over 5,000 times per second, and can satisfy the requirements for tracking highly dynamic targets in practical applications. Conclusion: The major contributions of this research are as follows: presenting an HLS - RTL co - design methodology for complex FPGA system development; designing an efficient phased - array beam - forming algorithm and its hardware implementation; and developing a flexible and scalable phased - array control system architecture that supports different array configurations. These findings are highly significant for beamforming technologies in phased - array radar and 5G communication systems, as well as for other FPGA applications requiring high - speed signal processing.

This paper describes the architecture of a safety system of the railway's interlocking equipment, which has been developed for Czech railways. The system will be used for the railway crossing gate. This system is based on FPGA blocks and has to fulfill the requirements for a fault tolerant system with a fail-safe function. The dual logic and TMR principle are used to increase its dependability. Several self-test and self-diagnostics features are used, such as an LFSR based built-in self-test, the FPGA readback and 1 out of 2 error detection codes. The functional logic uses a majority correction and the FPGA box reprogramming to precede the failure. The reliability analyses, models and reliability characteristics calculations of this system are described. Markov chain models are used for the reliability analyses. The TMR principles for fault tolerant system and the dual-TMR logic have been used in our design and both attempts are compared.

Field Programmable Gate Array Reliability Analysis Using the Dynamic Flowgraph Methodology

Digital watermarking is an innovative technique for intellectual property protection (IPP) of Field Programmable Gate Array (FPGA) designs. However, many of these techniques usually need manually extract marks from binary bit-files by the FPGA tool or exhaustive search to find out marks in the design, which results in inefficiency of the watermark verification. This paper presents a method to fast verify the authorship through extracting the content of the watermarked lookup tables from a binary bit-file. We demonstrate the proposed method on several Xilinx Virtex-II devices, and experimental results on the watermarked designs from the IWLS 2005 benchmarks show that the verification of authorship has high efficiency.

The reliability analysis of critical systems is often performed using fault tree analysis. Fault trees are analyzed using analytic approaches or Monte Carlo simulation. The usage of the analytic approaches is limited in few models and certain kinds of parameter distributions. In contrast to the analytic approaches, Monte Carlo simulation can be broadly used. However, Monte Carlo simulation is time-consuming because of the intensive computation. In this paper, a new model is presented which is called time-to-failure tree. Static and dynamic fault trees can be easily transformed into time-to-failure trees. In fact, each time-to-failure tree is a digital circuit, which can be synthesized to a field programmable gate array (FPGA). Therefore, Monte Carlo simulation can be significantly accelerated using FPGAs.