Improving reconfigurable systems reliability by combining periodical test and redundancy techniques: a case study

This paper revises and introduces to the field of reconfigurable computer systems, some traditional techniques used in the fields of fault-tolerance and testing of digital circuits. The target area is that of on-board spacecraft electronics, as this class of application is a good candidate for the use of reconfigurable computing technology. Fault tolerant strategies are used in order for the system to adapt itself to the severe conditions found in space. In addition, the paper describes some problems and possible solutions for the use of reconfigurable components, based on programmable logic, in space applications

Fault-tolerant fpga for mission-critical applications.

One of the devices that play a great role in electronic circuits design, specifically safety-critical design applications, is Field programmable Gate Arrays (FPGAs). This is because of its high performance, re-configurability and low development cost. FPGAs are used in many applications such as data processing, networks, automotive, space and industrial applications. Negative impacts on the reliability of such applications result from moving to smaller feature sizes in the latest FPGA architectures. This increases the need for fault-tolerant techniques to improve reliability and extend system lifetime of FPGA-based applications. In this thesis, two fault-tolerant techniques for FPGA-based applications are proposed with a built-in fault detection region. A low cost fault detection scheme is proposed for detecting faults using the fault detection region used in both schemes. The fault detection scheme primarily detects open faults in the programmable interconnect resources in the FPGAs. In addition, Stuck-At faults and Single Event Upsets (SEUs) fault can be detected. For fault recovery, each scheme has its own fault recovery approach. The first approach uses a spare module and a 2-to-1 multiplexer to recover from any fault detected. On the other hand, the second approach recovers from any fault detected using the property of Partial Reconfiguration (PR) in the FPGAs. It relies on identifying a Partially Reconfigurable block (P_b) in the FPGA that is used in the recovery process after the first faulty module is identified in the system. This technique uses only one location to recover from faults in any of the FPGA’s modules and the FPGA interconnects. Simulation results show that both techniques can detect and recover from open faults. In addition, Stuck-At faults and Single Event Upsets (SEUs) fault can also be detected. Finally, both techniques require low area overhead

Enhancing Real-time Embedded Image Processing Robustness on Reconfigurable Devices for Critical Applications

Nowadays, image processing is increasingly used in several application fields, such as biomedical, aerospace, or automotive. Within these fields, image processing is used to serve both non-critical and critical tasks. As example, in automotive, cameras are becoming key sensors in increasing car safety, driving assistance and driving comfort. They have been employed for infotainment (non-critical), as well as for some driver assistance tasks (critical), such as Forward Collision Avoidance, Intelligent Speed Control, or Pedestrian Detection. The complexity of these algorithms brings a challenge in real-time image processing systems, requiring high computing capacity, usually not available in processors for embedded systems. Hardware acceleration is therefore crucial, and devices such as Field Programmable Gate Arrays (FPGAs) best fit the growing demand of computational capabilities. These devices can assist embedded processors by significantly speeding-up computationally intensive software algorithms. Moreover, critical applications introduce strict requirements not only from the real-time constraints, but also from the device reliability and algorithm robustness points of view. Technology scaling is highlighting reliability problems related to aging phenomena, and to the increasing sensitivity of digital devices to external radiation events that can cause transient or even permanent faults. These faults can lead to wrong information processed or, in the worst case, to a dangerous system failure. In this context, the reconfigurable nature of FPGA devices can be exploited to increase the system reliability and robustness by leveraging Dynamic Partial Reconfiguration features. The research work presented in this thesis focuses on the development of techniques for implementing efficient and robust real-time embedded image processing hardware accelerators and systems for mission-critical applications. Three main challenges have been faced and will be discussed, along with proposed solutions, throughout the thesis: (i) achieving real-time performances, (ii) enhancing algorithm robustness, and (iii) increasing overall system's dependability. In order to ensure real-time performances, efficient FPGA-based hardware accelerators implementing selected image processing algorithms have been developed. Functionalities offered by the target technology, and algorithm's characteristics have been constantly taken into account while designing such accelerators, in order to efficiently tailor algorithm's operations to available hardware resources. On the other hand, the key idea for increasing image processing algorithms' robustness is to introduce self-adaptivity features at algorithm level, in order to maintain constant, or improve, the quality of results for a wide range of input conditions, that are not always fully predictable at design-time (e.g., noise level variations). This has been accomplished by measuring at run-time some characteristics of the input images, and then tuning the algorithm parameters based on such estimations. Dynamic reconfiguration features of modern reconfigurable FPGA have been extensively exploited in order to integrate run-time adaptivity into the designed hardware accelerators. Tools and methodologies have been also developed in order to increase the overall system dependability during reconfiguration processes, thus providing safe run-time adaptation mechanisms. In addition, taking into account the target technology and the environments in which the developed hardware accelerators and systems may be employed, dependability issues have been analyzed, leading to the development of a platform for quickly assessing the reliability and characterizing the behavior of hardware accelerators implemented on reconfigurable FPGAs when they are affected by such faults

A FPGA-Based Reconfigurable Software Architecture for Highly Dependable Systems

Nowadays, systems-on-chip are commonly equipped with reconfigurable hardware. The use of hybrid architectures based on a mixture of general purpose processors and reconfigurable components has gained importance across the scientific community allowing a significant improvement of computational performance. Along with the demand for performance, the great sensitivity of reconfigurable hardware devices to physical defects lead to the request of highly dependable and fault tolerant systems. This paper proposes an FPGA-based reconfigurable software architecture able to abstract the underlying hardware platform giving an homogeneous view of it. The abstraction mechanism is used to implement fault tolerance mechanisms with a minimum impact on the system performanc

A High Performance Fuzzy Logic Architecture for UAV Decision Making

The majority of Unmanned Aerial Vehicles (UAVs) in operation today are not truly autonomous, but are instead reliant on a remote human pilot. A high degree of autonomy can provide many advantages in terms of cost, operational resources and safety. However, one of the challenges involved in achieving autonomy is that of replicating the reasoning and decision making capabilities of a human pilot. One candidate method for providing this decision making capability is fuzzy logic. In this role, the fuzzy system must satisfy real-time constraints, process large quantities of data and relate to large knowledge bases. Consequently, there is a need for a generic, high performance fuzzy computation platform for UAV applications. Based on Lees’ [1] original work, a high performance fuzzy processing architecture, implemented in Field Programmable Gate Arrays (FPGAs), has been developed and is shown to outclass the performance of existing fuzzy processors

New Design Techniques for Dynamic Reconfigurable Architectures

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