Autonomous Recovery Of Reconfigurable Logic Devices Using Priority Escalation Of Slack

Field Programmable Gate Array (FPGA) devices offer a suitable platform for survivable hardware architectures in mission-critical systems. In this dissertation, active dynamic redundancy-based fault-handling techniques are proposed which exploit the dynamic partial reconfiguration capability of SRAM-based FPGAs. Self-adaptation is realized by employing reconfiguration in detection, diagnosis, and recovery phases. To extend these concepts to semiconductor aging and process variation in the deep submicron era, resilient adaptable processing systems are sought to maintain quality and throughput requirements despite the vulnerabilities of the underlying computational devices. A new approach to autonomous fault-handling which addresses these goals is developed using only a uniplex hardware arrangement. It operates by observing a health metric to achieve Fault Demotion using Recon- figurable Slack (FaDReS). Here an autonomous fault isolation scheme is employed which neither requires test vectors nor suspends the computational throughput, but instead observes the value of a health metric based on runtime input. The deterministic flow of the fault isolation scheme guarantees success in a bounded number of reconfigurations of the FPGA fabric. FaDReS is then extended to the Priority Using Resource Escalation (PURE) online redundancy scheme which considers fault-isolation latency and throughput trade-offs under a dynamic spare arrangement. While deep-submicron designs introduce new challenges, use of adaptive techniques are seen to provide several promising avenues for improving resilience. The scheme developed is demonstrated by hardware design of various signal processing circuits and their implementation on a Xilinx Virtex-4 FPGA device. These include a Discrete Cosine Transform (DCT) core, Motion Estimation (ME) engine, Finite Impulse Response (FIR) Filter, Support Vector Machine (SVM), and Advanced Encryption Standard (AES) blocks in addition to MCNC benchmark circuits. A iii significant reduction in power consumption is achieved ranging from 83% for low motion-activity scenes to 12.5% for high motion activity video scenes in a novel ME engine configuration. For a typical benchmark video sequence, PURE is shown to maintain a PSNR baseline near 32dB. The diagnosability, reconfiguration latency, and resource overhead of each approach is analyzed. Compared to previous alternatives, PURE maintains a PSNR within a difference of 4.02dB to 6.67dB from the fault-free baseline by escalating healthy resources to higher-priority signal processing functions. The results indicate the benefits of priority-aware resiliency over conventional redundancy approaches in terms of fault-recovery, power consumption, and resource-area requirements. Together, these provide a broad range of strategies to achieve autonomous recovery of reconfigurable logic devices under a variety of constraints, operating conditions, and optimization criteria

Reliable and Fault-Resilient Schemes for Efficient Radix-4 Complex Division

Complex division is commonly used in various applications in signal processing and control theory including astronomy and nonlinear RF measurements. Nevertheless, unless reliability and assurance are embedded into the architectures of such structures, the suboptimal (and thus erroneous) results could undermine the objectives of such applications. As such, in this thesis, we present schemes to provide complex number division architectures based on (Sweeney, Robertson, and Tocher) SRT-division with fault diagnosis mechanisms. Different fault resilient architectures are proposed in this thesis which can be tailored based on the eventual objectives of the designs in terms of area and time requirements, among which we pinpoint carefully the schemes based on recomputing with shifted operands (RESO) to be able to detect both natural and malicious faults and with proper modification achieve high throughputs. The design also implements a minimized look up table approach which favors in error detection based designs and provides high fault coverage with relatively-low overhead. Additionally, to benchmark the effectiveness of the proposed schemes, extensive fault diagnosis assessments are performed for the proposed designs through fault simulations and FPGA implementations; the design is implemented on Xilinx Spartan-VI and Xilinx Virtex-VI FPGA families

Lightweight Architectures for Reliable and Fault Detection Simon and Speck Cryptographic Algorithms on FPGA

The widespread use of sensitive and constrained applications necessitates lightweight (lowpower and low-area) algorithms developed for constrained nano-devices. However, nearly all of such algorithms are optimized for platform-based performance and may not be useful for diverse and flexible applications. The National Security Agency (NSA) has proposed two relatively-recent families of lightweight ciphers, i.e., Simon and Speck, designed as efficient ciphers on both hardware and software platforms. This paper proposes concurrent error detection schemes to provide reliable architectures for these two families of lightweight block ciphers. The research work on analyzing the reliability of these algorithms and providing fault diagnosis approaches has not been undertaken to date to the best of our knowledge. The main aim of the proposed reliable architectures is to provide high error coverage while maintaining acceptable area and power consumption overheads. To achieve this, we propose a variant of recomputing with encoded operands. These low-complexity schemes are suited for lowresource applications such as sensitive, constrained implantable and wearable medical devices. We perform fault simulations for the proposed architectures by developing a fault model framework. The architectures are simulated and analyzed on recent field-programmable grate array (FPGA) platforms, and it is shown that the proposed schemes provide high error coverage. The proposed low-complexity concurrent error detection schemes are a step forward towards more reliable architectures for Simon and Speck algorithms in lightweight, secure applications

Low-overhead fault-tolerant logic for field-programmable gate arrays

While allowing for the fabrication of increasingly complex and efficient circuitry, transistor shrinkage and count-per-device expansion have major downsides: chiefly increased variation, degradation and fault susceptibility. For this reason, design-time consideration of faults will have to be given to increasing numbers of electronic systems in the future to ensure yields, reliabilities and lifetimes remain acceptably high. Many mathematical operators commonly accelerated in hardware are suited to modification resulting in datapath error detection and correction capabilities with far lower area, performance and/or power consumption overheads than those incurred through the utilisation of more established, general-purpose fault tolerance methods such as modular redundancy. Field-programmable gate arrays are uniquely placed to allow further area savings to be made thanks to their dynamic reconfigurability. The majority of the technical work presented within this thesis is based upon a benchmark hardware accelerator---a matrix multiplier---that underwent several evolutions in order to detect and correct faults manifesting along its datapath at runtime. In the first instance, fault detectability in excess of 99% was achieved in return for 7.87% additional area and 45.5% extra latency. In the second, the ability to correct errors caused by those faults was added at the cost of 4.20% more area, while 50.7% of this---and 46.2% of the previously incurred latency overhead---was removed through the introduction of partial reconfiguration in the third. The fourth demonstrates further reductions in both area and performance overheads---of 16.7% and 8.27%, respectively---through systematic data width reduction by allowing errors of less than ±0.5% of the maximum output value to propagate.Open Acces

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

Scalable algorithms for software based self test using formal methods

textTransistor scaling has kept up with Moore's law with a doubling of the number of transistors on a chip. More logic on a chip means more opportunities for manufacturing defects to slip in. This, in turn, has made processor testing after manufacturing a significant challenge. At-speed functional testing, being completely non-intrusive, has been seen as the ideal way of testing chips. However for processor testing, generating instruction level tests for covering all faults is a challenge given the issue of scalability. Data-path faults are relatively easier to control and observe compared to control-path faults. In this research we present a novel method to generate instruction level tests for hard to detect control-path faults in a processor. We initially map the gate level stuck-at fault to the Register Transfer Level (RTL) and build an equivalent faulty RTL model. The fault activation and propagation constraints are captured using Control and Data Flow Graphs of the RTL as a Liner Temporal Logic (LTL) property. This LTL property is then negated and given to a Bounded Model Checker based on a Bit-Vector Satisfiability Module Theories (SMT) solver. From the counter-example to the property we can extract a sequence of instructions that activates the gate level fault and propagates the fault effect to one of the observable points in the design. Other than the user supplying instruction constraints, this approach is completely automatic and does not require any manual intervention. Not all the design behaviors are required to generate a test for a fault. We use this insight to scale our previous methodology further. Underapproximations are design abstractions that only capture a subset of the original design behaviors. The use of RTL for test generation affords us two types of under-approximations: bit-width reduction and operator approximation. These are abstractions that perform reductions based on semantics of the RTL design. We also explore structural reductions of the RTL, called path based search, where we search through error propagation paths incrementally. This approach increases the size of the test generation problem step by step. In this way the SMT solver searches through the state space piecewise rather than doing the entire search at once. Experimental results show that our methods are robust and scalable for generating functional tests for hard to detect faults.Electrical and Computer Engineerin

Design and application of reconfigurable circuits and systems

Open Acces

AI/ML Algorithms and Applications in VLSI Design and Technology

An evident challenge ahead for the integrated circuit (IC) industry in the nanometer regime is the investigation and development of methods that can reduce the design complexity ensuing from growing process variations and curtail the turnaround time of chip manufacturing. Conventional methodologies employed for such tasks are largely manual; thus, time-consuming and resource-intensive. In contrast, the unique learning strategies of artificial intelligence (AI) provide numerous exciting automated approaches for handling complex and data-intensive tasks in very-large-scale integration (VLSI) design and testing. Employing AI and machine learning (ML) algorithms in VLSI design and manufacturing reduces the time and effort for understanding and processing the data within and across different abstraction levels via automated learning algorithms. It, in turn, improves the IC yield and reduces the manufacturing turnaround time. This paper thoroughly reviews the AI/ML automated approaches introduced in the past towards VLSI design and manufacturing. Moreover, we discuss the scope of AI/ML applications in the future at various abstraction levels to revolutionize the field of VLSI design, aiming for high-speed, highly intelligent, and efficient implementations

Innovative Techniques for Testing and Diagnosing SoCs

We rely upon the continued functioning of many electronic devices for our everyday welfare, usually embedding integrated circuits that are becoming even cheaper and smaller with improved features. Nowadays, microelectronics can integrate a working computer with CPU, memories, and even GPUs on a single die, namely System-On-Chip (SoC). SoCs are also employed on automotive safety-critical applications, but need to be tested thoroughly to comply with reliability standards, in particular the ISO26262 functional safety for road vehicles. The goal of this PhD. thesis is to improve SoC reliability by proposing innovative techniques for testing and diagnosing its internal modules: CPUs, memories, peripherals, and GPUs. The proposed approaches in the sequence appearing in this thesis are described as follows: 1. Embedded Memory Diagnosis: Memories are dense and complex circuits which are susceptible to design and manufacturing errors. Hence, it is important to understand the fault occurrence in the memory array. In practice, the logical and physical array representation differs due to an optimized design which adds enhancements to the device, namely scrambling. This part proposes an accurate memory diagnosis by showing the efforts of a software tool able to analyze test results, unscramble the memory array, map failing syndromes to cell locations, elaborate cumulative analysis, and elaborate a final fault model hypothesis. Several SRAM memory failing syndromes were analyzed as case studies gathered on an industrial automotive 32-bit SoC developed by STMicroelectronics. The tool displayed defects virtually, and results were confirmed by real photos taken from a microscope. 2. Functional Test Pattern Generation: The key for a successful test is the pattern applied to the device. They can be structural or functional; the former usually benefits from embedded test modules targeting manufacturing errors and is only effective before shipping the component to the client. The latter, on the other hand, can be applied during mission minimally impacting on performance but is penalized due to high generation time. However, functional test patterns may benefit for having different goals in functional mission mode. Part III of this PhD thesis proposes three different functional test pattern generation methods for CPU cores embedded in SoCs, targeting different test purposes, described as follows: a. Functional Stress Patterns: Are suitable for optimizing functional stress during I Operational-life Tests and Burn-in Screening for an optimal device reliability characterization b. Functional Power Hungry Patterns: Are suitable for determining functional peak power for strictly limiting the power of structural patterns during manufacturing tests, thus reducing premature device over-kill while delivering high test coverage c. Software-Based Self-Test Patterns: Combines the potentiality of structural patterns with functional ones, allowing its execution periodically during mission. In addition, an external hardware communicating with a devised SBST was proposed. It helps increasing in 3% the fault coverage by testing critical Hardly Functionally Testable Faults not covered by conventional SBST patterns. An automatic functional test pattern generation exploiting an evolutionary algorithm maximizing metrics related to stress, power, and fault coverage was employed in the above-mentioned approaches to quickly generate the desired patterns. The approaches were evaluated on two industrial cases developed by STMicroelectronics; 8051-based and a 32-bit Power Architecture SoCs. Results show that generation time was reduced upto 75% in comparison to older methodologies while increasing significantly the desired metrics. 3. Fault Injection in GPGPU: Fault injection mechanisms in semiconductor devices are suitable for generating structural patterns, testing and activating mitigation techniques, and validating robust hardware and software applications. GPGPUs are known for fast parallel computation used in high performance computing and advanced driver assistance where reliability is the key point. Moreover, GPGPU manufacturers do not provide design description code due to content secrecy. Therefore, commercial fault injectors using the GPGPU model is unfeasible, making radiation tests the only resource available, but are costly. In the last part of this thesis, we propose a software implemented fault injector able to inject bit-flip in memory elements of a real GPGPU. It exploits a software debugger tool and combines the C-CUDA grammar to wisely determine fault spots and apply bit-flip operations in program variables. The goal is to validate robust parallel algorithms by studying fault propagation or activating redundancy mechanisms they possibly embed. The effectiveness of the tool was evaluated on two robust applications: redundant parallel matrix multiplication and floating point Fast Fourier Transform

Towards the development of flexible, reliable, reconfigurable, and high-performance imaging systems

Current FPGAs can implement large systems because of the high density of reconfigurable logic resources in a single chip. FPGAs are comprehensive devices that combine flexibility and high performance in the same platform compared to other platform such as General-Purpose Processors (GPPs) and Application Specific Integrated Circuits (ASICs). The flexibility of modern FPGAs is further enhanced by introducing Dynamic Partial Reconfiguration (DPR) feature, which allows for changing the functionality of part of the system while other parts are functioning. FPGAs became an important platform for digital image processing applications because of the aforementioned features. They can fulfil the need of efficient and flexible platforms that execute imaging tasks efficiently as well as the reliably with low power, high performance and high flexibility. The use of FPGAs as accelerators for image processing outperforms most of the current solutions. Current FPGA solutions can to load part of the imaging application that needs high computational power on dedicated reconfigurable hardware accelerators while other parts are working on the traditional solution to increase the system performance. Moreover, the use of the DPR feature enhances the flexibility of image processing further by swapping accelerators in and out at run-time. The use of fault mitigation techniques in FPGAs enables imaging applications to operate in harsh environments following the fact that FPGAs are sensitive to radiation and extreme conditions. The aim of this thesis is to present a platform for efficient implementations of imaging tasks. The research uses FPGAs as the key component of this platform and uses the concept of DPR to increase the performance, flexibility, to reduce the power dissipation and to expand the cycle of possible imaging applications. In this context, it proposes the use of FPGAs to accelerate the Image Processing Pipeline (IPP) stages, the core part of most imaging devices. The thesis has a number of novel concepts. The first novel concept is the use of FPGA hardware environment and DPR feature to increase the parallelism and achieve high flexibility. The concept also increases the performance and reduces the power consumption and area utilisation. Based on this concept, the following implementations are presented in this thesis: An implementation of Adams Hamilton Demosaicing algorithm for camera colour interpolation, which exploits the FPGA parallelism to outperform other equivalents. In addition, an implementation of Automatic White Balance (AWB), another IPP stage that employs DPR feature to prove the mentioned novelty aspects. Another novel concept in this thesis is presented in chapter 6, which uses DPR feature to develop a novel flexible imaging system that requires less logic and can be implemented in small FPGAs. The system can be employed as a template for any imaging application with no limitation. Moreover, discussed in this thesis is a novel reliable version of the imaging system that adopts novel techniques including scrubbing, Built-In Self Test (BIST), and Triple Modular Redundancy (TMR) to detect and correct errors using the Internal Configuration Access Port (ICAP) primitive. These techniques exploit the datapath-based nature of the implemented imaging system to improve the system's overall reliability. The thesis presents a proposal for integrating the imaging system with the Robust Reliable Reconfigurable Real-Time Heterogeneous Operating System (R4THOS) to get the best out of the system. The proposal shows the suitability of the proposed DPR imaging system to be used as part of the core system of autonomous cars because of its unbounded flexibility. These novel works are presented in a number of publications as shown in section 1.3 later in this thesis