Injecting FPGA Configuration Faults in Parallel

When using SRAM-based FPGA devices in safety critical applications testing against bitflips in the device configuration memory is essential. Often such tests are achieved by corrupting configuration memory bits of a running device, but this has many scalability, reliability, and flexibility challenges. In this paper, we present a framework and a concrete implementation of a parallel fault injection cluster that addresses these challenges. Scalability is addressed by using multiple identical FPGA devices, each testing a different region in parallel. Reliability is addressed by using reconfigurable system-on-chip devices, that are isolated from each other. Flexibility is addressed by using a pending commit structure, that continually checkpoints the overall experiment and allows elastic scaling. We test and showcase our approach by exhaustively flipping every bit in the configuration memory of the CHStone benchmark suite and a VivadoHLS generated k-means clustering image processing application. Our results show that: linear scaling is possible as the number of devices increases; the majority of error inducing bitflips in the k-means application do not significantly impact the output; and that the Xilinx Essential bits tool may miss some bits that can induce errors

On the Resilience of RTL NN Accelerators: Fault Characterization and Mitigation

Machine Learning (ML) is making a strong resurgence in tune with the massive generation of unstructured data which in turn requires massive computational resources. Due to the inherently compute- and power-intensive structure of Neural Networks (NNs), hardware accelerators emerge as a promising solution. However, with technology node scaling below 10nm, hardware accelerators become more susceptible to faults, which in turn can impact the NN accuracy. In this paper, we study the resilience aspects of Register-Transfer Level (RTL) model of NN accelerators, in particular, fault characterization and mitigation. By following a High-Level Synthesis (HLS) approach, first, we characterize the vulnerability of various components of RTL NN. We observed that the severity of faults depends on both i) application-level specifications, i.e., NN data (inputs, weights, or intermediate), NN layers, and NN activation functions, and ii) architectural-level specifications, i.e., data representation model and the parallelism degree of the underlying accelerator. Second, motivated by characterization results, we present a low-overhead fault mitigation technique that can efficiently correct bit flips, by 47.3% better than state-of-the-art methods.Comment: 8 pages, 6 figure

Multi-LSTM Acceleration and CNN Fault Tolerance

This thesis addresses the following two problems related to the field of Machine Learning: the acceleration of multiple Long Short Term Memory (LSTM) models on FPGAs and the fault tolerance of compressed Convolutional Neural Networks (CNN). LSTMs represent an effective solution to capture long-term dependencies in sequential data, like sentences in Natural Language Processing applications, video frames in Scene Labeling tasks or temporal series in Time Series Forecasting. In order to further boost their efficacy, especially in presence of long sequences, multiple LSTM models are utilized in a Hierarchical and Stacked fashion. However, because of their memory-bounded nature, efficient mapping of multiple LSTMs on a computing device becomes even more challenging. The first part of this thesis addresses the problem of mapping multiple LSTM models to a FPGA device by introducing a framework that modifies their memory requirements according to the target architecture. For the similar accuracy loss, the proposed framework maps multiple LSTMs with a performance improvement of 3x to 5x over state-of-the-art approaches. In the second part of this thesis, we investigate the fault tolerance of CNNs, another effective deep learning architecture. CNNs represent a dominating solution in image classification tasks, but suffer from a high performance cost, due to their computational structure. In fact, due to their large parameter space, fetching their data from main memory typically becomes a performance bottleneck. In order to tackle the problem, various techniques for their parameters compression have been developed, such as weight pruning, weight clustering and weight quantization. However, reducing the memory footprint of an application can lead to its data becoming more sensitive to faults. For this thesis work, we have conducted an analysis to verify the conditions for applying OddECC, a mechanism that supports variable strength and size ECCs for different memory regions. Our experiments reveal that compressed CNNs, which have their memory footprint reduced up to 86.3x by utilizing the aforementioned compression schemes, exhibit accuracy drops up to 13.56% in presence of random single bit faults

Speeding-up model-based fault injection of deep-submicron CMOS fault models through dynamic and partially reconfigurable FPGAS

Actualmente, las tecnologías CMOS submicrónicas son básicas para el desarrollo de los modernos sistemas basados en computadores, cuyo uso simplifica enormemente nuestra vida diaria en una gran variedad de entornos, como el gobierno, comercio y banca electrónicos, y el transporte terrestre y aeroespacial. La continua reducción del tamaño de los transistores ha permitido reducir su consumo y aumentar su frecuencia de funcionamiento, obteniendo por ello un mayor rendimiento global. Sin embargo, estas mismas características que mejoran el rendimiento del sistema, afectan negativamente a su confiabilidad. El uso de transistores de tamaño reducido, bajo consumo y alta velocidad, está incrementando la diversidad de fallos que pueden afectar al sistema y su probabilidad de aparición. Por lo tanto, existe un gran interés en desarrollar nuevas y eficientes técnicas para evaluar la confiabilidad, en presencia de fallos, de sistemas fabricados mediante tecnologías submicrónicas. Este problema puede abordarse por medio de la introducción deliberada de fallos en el sistema, técnica conocida como inyección de fallos. En este contexto, la inyección basada en modelos resulta muy interesante, ya que permite evaluar la confiabilidad del sistema en las primeras etapas de su ciclo de desarrollo, reduciendo por tanto el coste asociado a la corrección de errores. Sin embargo, el tiempo de simulación de modelos grandes y complejos imposibilita su aplicación en un gran número de ocasiones. Esta tesis se centra en el uso de dispositivos lógicos programables de tipo FPGA (Field-Programmable Gate Arrays) para acelerar los experimentos de inyección de fallos basados en simulación por medio de su implementación en hardware reconfigurable. Para ello, se extiende la investigación existente en inyección de fallos basada en FPGA en dos direcciones distintas: i) se realiza un estudio de las tecnologías submicrónicas existentes para obtener un conjunto representativo de modelos de fallos transitoriosAndrés Martínez, DD. (2007). Speeding-up model-based fault injection of deep-submicron CMOS fault models through dynamic and partially reconfigurable FPGAS [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/1943Palanci

Hardware Fault Injection

Hardware fault injection is the widely accepted approach to evaluate the behavior of a circuit in the presence of faults. Thus, it plays a key role in the design of robust circuits. This chapter presents a comprehensive review of hardware fault injection techniques, including physical and logical approaches. The implementation of effective fault injection systems is also analyzed. Particular emphasis is made on the recently developed emulation-based techniques, which can provide large flexibility along with unprecedented levels of performance. These capabilities provide a way to tackle reliability evaluation of complex circuits.Publicad

An Approach to Emulate and Validate the Effects of Single Event Upsets using the PREDICT FUTRE Hardware Integrated Framework

Due to the advances in electronics design automation industry, worldwide, the integrated approach to model and emulate the single event effects due to cosmic radiation, in particular single event upsets or single event transients is gaining momentum. As of now, no integrated methodology to inject the fault in parallel to functional test vectors or to estimate the effects of radiation for a selected function in system on chip at design phase exists. In this paper, a framework, PRogrammable single Event effects Demonstrator for dIgital Chip Technologies (PREDICT) failure assessment for radiation effects is developed using a hardware platform and aided by genetic algorithms addressing all the above challenges. A case study is carried out to evaluate the frameworks capability to emulate the effects of radiation using the co-processor as design under test (DUT) function. Using the ML605 and Virtex-6 evaluation board for single and three particle simulations with the layered atmospheric conditions, the proposed framework consumes approximately 100 min and 300 min, respectively; it consumes 600 min for 3 particle random atmospheric conditions, using the 64 GB RAM, 64-bit operating system with 3.1 GHz processor based workstation. The framework output transforms the 4 MeVcm2/mg linear energy transfer to a single event transient pulse width of 2 μs with 105 amplification factor for visualisation, which matches well with the existing experimental results data. Using the framework, the effects of radiation for the co-processing module are estimated during the design phase and the success rate of the DUT is found to be 48 per cent

Built-In Self-Test Quality Assessment Using Hardware Fault Emulation in FPGAs

This paper addresses the problem of test quality assessment, namely of BIST solutions, implemented in FPGA and/or in ASIC, through Hardware Fault Emulation (HFE). A novel HFE methodology and tool is proposed, that, using partial reconfiguration, efficiently measures the quality of the BIST solution. The proposed HFE methodology uses Look-Up Tables (LUTs) fault models and is performed using local partial reconfiguration for fault injection on Xilinx(TM) Virtex and/or Spartan FPGA components, with small binary files. For ASIC cores, HFE is used to validate test vector selection to achieve high fault coverage on the physical structure. The methodology is fully automated. Results on ISCAS benchmarks and on an ARM core show that HFE can be orders of magnitude faster than software fault simulation or fully reconfigurable hardware fault emulation

Enhancement of fault injection techniques based on the modification of VHDL code

Deep submicrometer devices are expected to be increasingly sensitive to physical faults. For this reason, fault-tolerance mechanisms are more and more required in VLSI circuits. So, validating their dependability is a prior concern in the design process. Fault injection techniques based on the use of hardware description languages offer important advantages with regard to other techniques. First, as this type of techniques can be applied during the design phase of the system, they permit reducing the time-to-market. Second, they present high controllability and reachability. Among the different techniques, those based on the use of saboteurs and mutants are especially attractive due to their high fault modeling capability. However, implementing automatically these techniques in a fault injection tool is difficult. Especially complex are the insertion of saboteurs and the generation of mutants. In this paper, we present new proposals to implement saboteurs and mutants for models in VHDL which are easy-to-automate, and whose philosophy can be generalized to other hardware description languages.Baraza Calvo, JC.; Gracia-Morán, J.; Blanc Clavero, S.; Gil Tomás, DA.; Gil Vicente, PJ. (2008). Enhancement of fault injection techniques based on the modification of VHDL code. IEEE Transactions on Very Large Scale Integration (VLSI) Systems. 16(6):693-706. doi:10.1109/TVLSI.2008.2000254S69370616