Characterization of Hardening by Design Techniques on Commercial, Small Feature Sized Field-Programmable Gate Arrays

In this thesis, a methodology is developed to experimentally test and evaluate a programmable logic device unde r gamma irradiation. The purpose of which is to determine the radiation effects and characterize the improvements of various hardening by design techniques. The techniques analyzed in this thesis include Error Correction Coding (ECC) and Triple Modular Redundancy (TMR). The TMR circuit includes three different functional implementations of adders compared to TMR voted circuits of those same adders. The TMR is implemented with the same functional adders and as a Functional TMR (FTMR) with three different function adders that are voted on. The three functional adders are: a behavioral adder that allows the FPGA synthesis software to create the implementation, a ripple carry adder that consists of multiple single bit full adders linked together, and a carry look ahead adder that operates the fastest by using an algorithm that creates generate and propagate signals. These adders are connected to single voter TMR and FTMR circuits to evaluate the improvements that could be obtained. The ECC circuit includes Block RAM (BRAM) and Distributed RAM memory elements that are loaded both with ECC and non-error corrected data. The circuit is designed to check for errors in memory data, stuck bit values in the memory, and the performance improvements that ECC provides the system

Fault and Defect Tolerant Computer Architectures: Reliable Computing With Unreliable Devices

This research addresses design of a reliable computer from unreliable device technologies. A system architecture is developed for a fault and defect tolerant (FDT) computer. Trade-offs between different techniques are studied and yield and hardware cost models are developed. Fault and defect tolerant designs are created for the processor and the cache memory. Simulation results for the content-addressable memory (CAM)-based cache show 90% yield with device failure probabilities of 3 x 10(-6), three orders of magnitude better than non fault tolerant caches of the same size. The entire processor achieves 70% yield with device failure probabilities exceeding 10(-6). The required hardware redundancy is approximately 15 times that of a non-fault tolerant design. While larger than current FT designs, this architecture allows the use of devices much more likely to fail than silicon CMOS. As part of model development, an improved model is derived for NAND Multiplexing. The model is the first accurate model for small and medium amounts of redundancy. Previous models are extended to account for dependence between the inputs and produce more accurate results

A new architecture for single-event upset detection & reconfiguration of SRAM-based FPGAs

Field Programmable Gate Arrays (FPGA) are used in a variety of applications, ranging from consumer electronics to devices in spacecrafts because of their flexibility in achieving requirements such as low cost, high performance, and fast turnaround. SRAM-based FPGAs can experience single bit flips in the configuration memory due to high-energy neutrons or alpha particles hitting critical nodes in the SRAM cells, by transferring enough energy to effect the change. High energy particles can be emitted by cosmic radiation or traces of radioactive elements in device packaging. The result of this could range from unwanted functional or data modification, data loss in the system, to damage to the cell where the charged particle makes impact. This phenomenon is known as a Single Event Upset (SEU) and makes fault tolerance a critical requirement in FPGA design. This research proposes a shift in architecture from current SRAM-based FPGAs such as Xilinx Virtex. The proposed architecture includes an inherent SEU detection through parity checking of the configuration memory. The inherent SEU detection sets a syndrome flag when an odd number of bit flips occur within a data frame of the configuration memory. To correct a fault, the FPGA the affected data frame is partially reconfigured. Existing and proposed solutions include: Triple Modular Redundancy (TMR) systems; readback and compare the configuration memory; and periodically reprogramming the entire configuration memory, also known as scrubbing. The advantages afforded by the proposed architecture over existing solutions include: faster error detection and correction latency over the readback method and better area and power overhead over TMR

EDAC software implementation to protect small satellites memory

Radiation is a well-known problem for satellites in space. It can produce different negative effects on electronic components which can provoke errors and failures. Therefore, mitigating these effects is especially important for the success of space missions. One of the techniques to increase the reliability of memory chips and reduce transient errors and permanent faults is Error Detection and Correction (EDAC). EDAC codes are characterised by the use of redundancy to detect and correct errors. This final project consists in the implementation of a software EDAC algorithm to protect the main memory of a microcontroller. The implementation requirements and the issues of software EDAC are described and the test results are commented

Radiation hard FPGA configuration techniques using silicon on sapphire

 Once entirely the domain of space-borne applications, the effects of high energy charged particles on electronics systems is now also a concern for terrestrial devices. Reconfigurable components such as FPGAs are particularly vulnerable to radiation single event effects (SEU) as they carry a large amount of memory within a relatively small amount of circuit area. This thesis presents a Silicon on Insulator (SOI) based configuration memory system in a radiation hard reconfiguration system. The SOI technology used in this particular work is Silicon on Sapphire, where Sapphire is used as the body insulator. A non-volatile storage cell, able to be manufactured in a standard single polysilicon SOI CMOS process with no special layers, is combined with a Schmitt amplifier which result a final structure that exhibits two unique characteristics enhancing its resistance to radiation. Firstly, it is impossible for a radiation induced event to permanently flip the configuration state. Secondly, a partial de-programming resulting in a reduction in the magnitude of the storage cell voltage causes a large change in static current that can be very easily detected using a conventional sense amplifier. A simple current detector of the type used in conventional RAM circuits allows the configuration memory to be set up to exhibit self-correcting, or “auto-scrubbing” behavior. While the combination of SOI EEPROM and Schmitt exhibits high intrinsic resistance to radiation induced errors, it is still possible for a sequence of two particle strikes to cause the configuration value to be lost. Estimates are made of the Soft error Rate (SER) performance of the overall configuration memory structure. A trial layout of a configurable Look Up Table (LUT) is presented as an example of how the SOS EEPROM configuration cell would be deployed in a real system

Cross-layer Soft Error Analysis and Mitigation at Nanoscale Technologies

This thesis addresses the challenge of soft error modeling and mitigation in nansoscale technology nodes and pushes the state-of-the-art forward by proposing novel modeling, analyze and mitigation techniques. The proposed soft error sensitivity analysis platform accurately models both error generation and propagation starting from a technology dependent device level simulations all the way to workload dependent application level analysis

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