Interconnect yield analysis and fault tolerance for field programmable gate arrays

Imperial Users onl

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

Using Relocatable Bitstreams for Fault Tolerance

This research develops a method for relocating reconfigurable modules on the Virtex-II (Pro) family of Field Programmable Gate Arrays (FPGAs). A bitstream translation program is developed which correctly changes the location of a partial bitstream that implements a module on the FPGA. To take advantage of relocatable modules, three fault-tolerance circuit designs are developed and tested. This circuit can operate through a fault by efficiently removing the faulty module and replacing it with a relocated module without faults. The FPGA can recover from faults at a known location, without the need for external intervention using an embedded fault recovery system. The recovery system uses an internal PowerPC to relocate the modules and reprogram the FPGA. Due to the limited architecture of the target FPGA and Xilinx tool errors, an FPGA with automatic fault recovery could not be demonstrated. However, the various components needed to do this type of recovery have been implemented and demonstrated individually

Reliable and High-Performance Hardware Architectures for the Advanced Encryption Standard/Galois Counter Mode

The high level of security and the fast hardware and software implementations of the Advanced Encryption Standard (AES) have made it the first choice for many critical applications. Since its acceptance as the adopted symmetric-key algorithm, the AES has been utilized in various security-constrained applications, many of which are power and resource constrained and require reliable and efficient hardware implementations. In this thesis, first, we investigate the AES algorithm from the concurrent fault detection point of view. We note that in addition to the efficiency requirements of the AES, it must be reliable against transient and permanent internal faults or malicious faults aiming at revealing the secret key. This reliability analysis and proposing efficient and effective fault detection schemes are essential because fault attacks have become a serious concern in cryptographic applications. Therefore, we propose, design, and implement various novel concurrent fault detection schemes for different AES hardware architectures. These include different structure-dependent and independent approaches for detecting single and multiple stuck-at faults using single and multi-bit signatures. The recently standardized authentication mode of the AES, i.e., Galois/Counter Mode (GCM), is also considered in this thesis. We propose efficient architectures for the AES-GCM algorithm. In this regard, we investigate the AES algorithm and we propose low-complexity and low-power hardware implementations for it, emphasizing on its nonlinear transformation, i.e., SubByes (S-boxes). We present new formulations for this transformation and through exhaustive hardware implementations, we show that the proposed architectures outperform their counterparts in terms of efficiency. Moreover, we present parallel, high-performance new schemes for the hardware implementations of the GCM to improve its throughput and reduce its latency. The performance of the proposed efficient architectures for the AES-GCM and their fault detection approaches are benchmarked using application-specific integrated circuit (ASIC) and field-programmable gate array (FPGA) hardware platforms. Our comparison results show that the proposed hardware architectures outperform their existing counterparts in terms of efficiency and fault detection capability

Strategies for neural networks in ballistocardiography with a view towards hardware implementation

A thesis submitted for the degree of Doctor of Philosophy at the University of LutonThe work described in this thesis is based on the results of a clinical trial conducted by the research team at the Medical Informatics Unit of the University of Cambridge, which show that the Ballistocardiogram (BCG) has prognostic value in detecting impaired left ventricular function before it becomes clinically overt as myocardial infarction leading to sudden death. The objective of this study is to develop and demonstrate a framework for realising an on-line BCG signal classification model in a portable device that would have the potential to find pathological signs as early as possible for home health care. Two new on-line automatic BeG classification models for time domain BeG classification are proposed. Both systems are based on a two stage process: input feature extraction followed by a neural classifier. One system uses a principal component analysis neural network, and the other a discrete wavelet transform, to reduce the input dimensionality. Results of the classification, dimensionality reduction, and comparison are presented. It is indicated that the combined wavelet transform and MLP system has a more reliable performance than the combined neural networks system, in situations where the data available to determine the network parameters is limited. Moreover, the wavelet transfonn requires no prior knowledge of the statistical distribution of data samples and the computation complexity and training time are reduced. Overall, a methodology for realising an automatic BeG classification system for a portable instrument is presented. A fully paralJel neural network design for a low cost platform using field programmable gate arrays (Xilinx's XC4000 series) is explored. This addresses the potential speed requirements in the biomedical signal processing field. It also demonstrates a flexible hardware design approach so that an instrument's parameters can be updated as data expands with time. To reduce the hardware design complexity and to increase the system performance, a hybrid learning algorithm using random optimisation and the backpropagation rule is developed to achieve an efficient weight update mechanism in low weight precision learning. The simulation results show that the hybrid learning algorithm is effective in solving the network paralysis problem and the convergence is much faster than by the standard backpropagation rule. The hidden and output layer nodes have been mapped on Xilinx FPGAs with automatic placement and routing tools. The static time analysis results suggests that the proposed network implementation could generate 2.7 billion connections per second performance

Design Disjunction for Resilient Reconfigurable Hardware

Contemporary reconfigurable hardware devices have the capability to achieve high performance, power efficiency, and adaptability required to meet a wide range of design goals. With scaling challenges facing current complementary metal oxide semiconductor (CMOS), new concepts and methodologies supporting efficient adaptation to handle reliability issues are becoming increasingly prominent. Reconfigurable hardware and their ability to realize self-organization features are expected to play a key role in designing future dependable hardware architectures. However, the exponential increase in density and complexity of current commercial SRAM-based field-programmable gate arrays (FPGAs) has escalated the overhead associated with dynamic runtime design adaptation. Traditionally, static modular redundancy techniques are considered to surmount this limitation; however, they can incur substantial overheads in both area and power requirements. To achieve a better trade-off among performance, area, power, and reliability, this research proposes design-time approaches that enable fine selection of redundancy level based on target reliability goals and autonomous adaptation to runtime demands. To achieve this goal, three studies were conducted: First, a graph and set theoretic approach, named Hypergraph-Cover Diversity (HCD), is introduced as a preemptive design technique to shift the dominant costs of resiliency to design-time. In particular, union-free hypergraphs are exploited to partition the reconfigurable resources pool into highly separable subsets of resources, each of which can be utilized by the same synthesized application netlist. The diverse implementations provide reconfiguration-based resilience throughout the system lifetime while avoiding the significant overheads associated with runtime placement and routing phases. Evaluation on a Motion-JPEG image compression core using a Xilinx 7-series-based FPGA hardware platform has demonstrated the potential of the proposed FT method to achieve 37.5% area saving and up to 66% reduction in power consumption compared to the frequently-used TMR scheme while providing superior fault tolerance. Second, Design Disjunction based on non-adaptive group testing is developed to realize a low-overhead fault tolerant system capable of handling self-testing and self-recovery using runtime partial reconfiguration. Reconfiguration is guided by resource grouping procedures which employ non-linear measurements given by the constructive property of f-disjunctness to extend runtime resilience to a large fault space and realize a favorable range of tradeoffs. Disjunct designs are created using the mosaic convergence algorithm developed such that at least one configuration in the library evades any occurrence of up to d resource faults, where d is lower-bounded by f. Experimental results for a set of MCNC and ISCAS benchmarks have demonstrated f-diagnosability at the individual slice level with average isolation resolution of 96.4% (94.4%) for f=1 (f=2) while incurring an average critical path delay impact of only 1.49% and area cost roughly comparable to conventional 2-MR approaches. Finally, the proposed Design Disjunction method is evaluated as a design-time method to improve timing yield in the presence of large random within-die (WID) process variations for application with a moderately high production capacity

Autonomously Reconfigurable Artificial Neural Network on a Chip

Artificial neural network (ANN), an established bio-inspired computing paradigm, has proved very effective in a variety of real-world problems and particularly useful for various emerging biomedical applications using specialized ANN hardware. Unfortunately, these ANN-based systems are increasingly vulnerable to both transient and permanent faults due to unrelenting advances in CMOS technology scaling, which sometimes can be catastrophic. The considerable resource and energy consumption and the lack of dynamic adaptability make conventional fault-tolerant techniques unsuitable for future portable medical solutions. Inspired by the self-healing and self-recovery mechanisms of human nervous system, this research seeks to address reliability issues of ANN-based hardware by proposing an Autonomously Reconfigurable Artificial Neural Network (ARANN) architectural framework. Leveraging the homogeneous structural characteristics of neural networks, ARANN is capable of adapting its structures and operations, both algorithmically and microarchitecturally, to react to unexpected neuron failures. Specifically, we propose three key techniques --- Distributed ANN, Decoupled Virtual-to-Physical Neuron Mapping, and Dual-Layer Synchronization --- to achieve cost-effective structural adaptation and ensure accurate system recovery. Moreover, an ARANN-enabled self-optimizing workflow is presented to adaptively explore a "Pareto-optimal" neural network structure for a given application, on the fly. Implemented and demonstrated on a Virtex-5 FPGA, ARANN can cover and adapt 93% chip area (neurons) with less than 1% chip overhead and O(n) reconfiguration latency. A detailed performance analysis has been completed based on various recovery scenarios