A Design Approach for Soft Errors Protection in Real-Time Systems

This paper proposes the use of metrics to refine system design for soft errors protection in system on chip architectures. Specifically this research shows the use of metrics in design space exploration that highlight where in the structure of the model and at what point in the behaviour, protection is needed against soft errors. As these metrics improve the ability of the system to provide functionality, they are referred to here as reliability metrics. Previous approaches to prevent soft errors focused on recovery after detection. Almost no research has been directed towards preventive measures. But in real-time systems, deadlines are performance requirements that absolutely must be met and a missed deadline constitutes an erroneous action and a possible system failure. This paper focuses on a preventive approach as a solution rather than recovery after detection. The intention of this research is to prevent serious loss of system functionality or system failure though it may not be able to eliminate the impact of soft errors completely

On the Prediction of Hardware Security Properties of HLS Designs Using Graph Neural Networks

High-level synthesis (HLS) tools have provided significant productivity enhancements to the design flow of digital systems in recent years, resulting in highly-optimized circuits, in terms of area and latency. Given the evolution of hardware attacks, which can render them vulnerable, it is essential to consider security as a significant aspect of the HLS design flow. Yet the need to evaluate a huge number of functionally equivalent de-signs of the HLS design space challenges hardware security evaluation methods (e.g., fault injection - FI campaigns). In this work, we propose an evaluation methodology of hardware security properties of HLS-produced designs using state-of-the-art Graph Neural Network (GNN) approaches that achieves significant speedup and better scalability than typical evaluation methods (such as FI). We demonstrate the proposed methodology on a Double Modular Redundancy (DMR) coun-termeasure applied on an AES SBox implementation, en-hanced by diversifying the redundant modules through HLS directives. The experimental results show that GNNs can be efficiently trained to predict important hardware security met-rics concerning fault attacks (e.g., critical and detection error rates), by using regression. The proposed method predicts the fault vulnerability metrics of the HLS-based designs with high R-squared scores and achieves huge speedup compared to fault injection once the training of the GNN is completed.Comment: 6 pages, 2 figures, 3 tables, submitted to 2023 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT

Low-cost error detection through high-level synthesis

System-on-chip design is becoming increasingly complex as technology scaling enables more and more functionality on a chip. This scaling and complexity has resulted in a variety of reliability and validation challenges including logic bugs, hot spots, wear-out, and soft errors. To make matters worse, as we reach the limits of Dennard scaling, efforts to improve system performance and energy efficiency have resulted in the integration of a wide variety of complex hardware accelerators in SoCs. Thus the challenge is to design complex, custom hardware that is efficient, but also correct and reliable. High-level synthesis shows promise to address the problem of complex hardware design by providing a bridge from the high-productivity software domain to the hardware design process. Much research has been done on high-level synthesis efficiency optimizations. This thesis shows that high-level synthesis also has the power to address validation and reliability challenges through two solutions. One solution for circuit reliability is modulo-3 shadow datapaths: performing lightweight shadow computations in modulo-3 space for each main computation. We leverage the binding and scheduling flexibility of high-level synthesis to detect control errors through diverse binding and minimize area cost through intelligent checkpoint scheduling and modulo-3 reducer sharing. We introduce logic and dataflow optimizations to further reduce cost. We evaluated our technique with 12 high-level synthesis benchmarks from the arithmetic-oriented PolyBench benchmark suite using FPGA emulated netlist-level error injection. We observe coverages of 99.1% for stuck-at faults, 99.5% for soft errors, and 99.6% for timing errors with a 25.7% area cost and negligible performance impact. Leveraging a mean error detection latency of 12.75 cycles (4150x faster than end result check) for soft errors, we also explore a rollback recovery method with an additional area cost of 28.0%, observing a 175x increase in reliability against soft errors. Another solution for rapid post-silicon validation of accelerator designs is Hybrid Quick Error Detection (H-QED): inserting signature generation logic in a hardware design to create a heavily compressed signature stream that captures the internal behavior of the design at a fine temporal and spatial granularity for comparison with a reference set of signatures generated by high-level simulation to detect bugs. Using H-QED, we demonstrate an improvement in error detection latency (time elapsed from when a bug is activated to when it manifests as an observable failure) of two orders of magnitude and a threefold improvement in bug coverage compared to traditional post-silicon validation techniques. H-QED also uncovered previously unknown bugs in the CHStone benchmark suite, which is widely used by the HLS community. H-QED incurs less than 10% area overhead for the accelerator it validates with negligible performance impact, and we also introduce techniques to minimize any possible intrusiveness introduced by H-QED

Toward high-level synthesis of reliable circuits through low-cost modulo shadow datapaths

With transistor dimensions shrinking to the atomic scale, a plethora of new reliability problems presents a barrier to continued Moore’s law scaling. Traditional modular redundancy techniques with 2x and 3x area cost eliminate the area reduction benefits of such scaling. In this study, we take a partial redundancy approach to the reliability problem for arithmetic-orientated datapaths by performing lightweight shadow computations in the mod-b space, where b is the base of our modulo residue, for each main computation. We leverage the binding and scheduling flexibility of high-level synthesis to detect control errors through diverse binding and minimize area cost through intelligent checkpoint scheduling and modulo-b reducer sharing. We introduce logic and dataflow optimizations to further reduce cost. We evaluated our technique with 12 high-level synthesis benchmarks from the arithmetic- oriented PolyBench benchmark suite using FPGA emulated netlist-level error injection. When b = 3, we observe coverages of 99.2% for stuck-at faults, 99.5% for soft errors, and 99.8% for timing errors with a 25.7% area cost and negligible performance impact. When b = 5, we observe coverages of 99.4% for stuck-at faults, 99.8% for soft errors, and 99.9% for timing errors with a 48.5% area cost and negligible performance impact. Leveraging a mean error detection latency of 13.92 and 14.96 cycles, with both mod-3 and mod-5 units respectively (2554x faster than end result check) for soft errors, we also explore a rollback recovery method with an additional area cost of 28.0% for both cases, observing 411x increase in reliability against soft errors

Early Dependability Analysis of FPGA-Based Space Applications Using Formal Verification

SRAM-based FPGAs are increasingly attractive in the aerospace industry for their field programmability and low cost. Unfortunately, they suffer from cosmic radiation induced Single Event Effects (SEEs). In safety-critical applications, the dependability of the design is a prime concern since failures may have catastrophic consequences. Hence, an early analysis of dependability of such safety-critical applications will enable designers to develop systems that meet high dependability requirements, such as the DO-254 standard. In this thesis, we propose a high-level dependability and performability analysis methodology based on probabilistic model checking. Compared to the pen-and-pencil and discrete-event simulation approach, our methodology is more accurate due to the use of an automated formal verification technique. Moreover, compared to fault injection or beam testing, analysis at early design stages can guide designers to build more reliable designs reducing the overall cost and effort. The proposed methodology can perform three different types of analysis: evaluation of available design options, optimization of scrub intervals while satisfying its design assurance level requirements, and optimal partitioning of Triple-Modular Redundant (TMR) Systems. Such analysis can also guide designers to adopt proper mitigation technique(s), such as rescheduling, TMR, TMR with less frequent scrubs, or even can help to decide the number of TMR partitions for a given scrub intervals. Starting from a high-level description of a system, based on the preferred analysis, a Markov model or Markov (reward) model is constructed from the extracted Control Data Flow Graph (CDFG) and the failure/mitigation parameters for the targeted FPGA. Such modeling and exhaustive analysis elaborated using a probabilistic model checking technique can capture all the failures and repairs possible (according to some general model) in the system within the radiation environment. To illustrate the applicability of the proposed approach, we present our quantitative analysis obtained from DSP benchmark circuits

Robust and reliable hardware accelerator design through high-level synthesis

System-on-chip design is becoming increasingly complex as technology scaling enables more and more functionality on a chip. This scaling-driven complexity has resulted in a variety of reliability and validation challenges including logic bugs, hot spots, wear-out, and soft errors. To make matters worse, as we reach the limits of Dennard scaling, efforts to improve system performance and energy efficiency have resulted in the integration of a wide variety of complex hardware accelerators in SoCs. Thus the challenge is to design complex, custom hardware that is efficient, but also correct and reliable. High-level synthesis shows promise to address the problem of complex hardware design by providing a bridge from the high-productivity software domain to the hardware design process. Much research has been done on high-level synthesis efficiency optimizations. This dissertation shows that high-level synthesis also has the power to address validation and reliability challenges through three automated solutions targeting three key stages in the hardware design and use cycle: pre-silicon debugging, post-silicon validation, and post-deployment error detection. Our solution for rapid pre-silicon debugging of accelerator designs is hybrid tracing: comparing a datapath-level trace of hardware execution with a reference software implementation at a fine temporal and spatial granularity to detect logic bugs. An integrated backtrace process delivers source-code meaning to the hardware designer, pinpointing the location of bug activation and providing a strong hint for potential bug fixes. Experimental results show that we are able to detect and aid in localization of logic bugs from both C/C++ specifications as well as the high-level synthesis engine itself. A variation of this solution tailored for rapid post-silicon validation of accelerator designs is hybrid hashing: inserting signature generation logic in a hardware design to create a heavily compressed signature stream that captures the internal behavior of the design at a fine temporal and spatial granularity for comparison with a reference set of signatures generated by high-level simulation to detect bugs. Using hybrid hashing, we demonstrate an improvement in error detection latency (time elapsed from when a bug is activated to when it manifests as an observable failure) of two orders of magnitude and a threefold improvement in bug coverage compared to traditional post-silicon validation techniques. Hybrid hashing also uncovered previously unknown bugs in the CHStone benchmark suite, which is widely used by the HLS community. Hybrid hashing incurs less than 10% area overhead for the accelerator it validates with negligible performance impact, and we also introduce techniques to minimize any possible intrusiveness introduced by hybrid hashing. Finally, our solution for post-deployment error detection is modulo-3 shadow datapaths: performing lightweight shadow computations in modulo-3 space for each main computation. We leverage the binding and scheduling flexibility of high-level synthesis to detect control errors through diverse binding and minimize area cost through intelligent checkpoint scheduling and modulo-3 reducer sharing. We introduce logic and dataflow optimizations to further reduce cost. We evaluated our technique with 12 high-level synthesis benchmarks from the arithmetic-oriented PolyBench benchmark suite using FPGA emulated netlist-level error injection. We observe coverages of 99.1% for stuck-at faults, 99.5% for soft errors, and 99.6% for timing errors with a 25.7% area cost and negligible performance impact. Leveraging a mean error detection latency of 12.75 cycles (4150× faster than end result check) for soft errors, we also explore a rollback recovery method with an additional area cost of 28.0%, observing a 175× increase in reliability against soft errors. While the area cost of our modulo shadow datapaths is much better than traditional modular redundancy approaches, we want to maximize the applicability of our approach. To this end, we take a dive into gate-level architectural design for modulo arithmetic functional units. We introduce new low-cost gate-level architectures for all four key functional units in a shadow datapath: (1) a modulo reduction algorithm that generates architectures consisting entirely of full-adder standard cells; (2) minimum-area modulo adder and subtractor architectures; (3) an array-based modulo multiplier design; and (4) a modulo equality comparator that handles the residue encoding produced by the above. We compare our new functional units to the previous state-of-the-art approach, observing a 12.5% reduction in area and a 47.1% reduction in delay for a 32-bit mod-3 reducer; that our reducer costs, which tend to dominate shadow datapath costs, do not increase with larger modulo bases; and that for modulo-15 and above, all of our modulo functional units have better area and delay then their previous counterparts. We also demonstrate the practicality of our approach by designing a custom shadow datapath for error detection of a multiply accumulate functional unit, which has an area overhead of only 12% for a 32-bit main datapath and 2-bit modulo-3 shadow datapath. Taking our reliability solution further, we look at the bigger picture of modulo shadow datapaths combined with other solutions at different abstraction layers, looking to answer the following question: Given all of the existing reliability improvement techniques for application-specific hardware accelerators, what techniques or combinations of techniques are the most cost-effective? To answer this question, we consider a soft error fault model and empirically evaluate cross-layer combinations of ABFT, EDDI, and modulo shadow datapaths in the context of high-level synthesis; parity in logic synthesis; and flip-flop hardening techniques at the physical design level. We measure the reliability benefit and area, energy, and performance cost of each technique individually and for interesting technique combinations through FPGA emulated fault-injection and physical place-and-route. Our results show that a combination of parity and flip-flop hardening is the most cost-effective in general with an average 1.3% area cost and 5.7% energy cost for a 50× improvement in reliability. The addition of modulo-3 shadow datapaths to this combination provides some additional benefit for some applications, even without considering its combinational logic, stuck-at fault, and timing error protection benefits. We also observe new efficiency challenges for ABFT and EDDI when used for hardware accelerators

Soft Error Analysis and Mitigation at High Abstraction Levels

Radiation-induced soft errors, as one of the major reliability challenges in future technology nodes, have to be carefully taken into consideration in the design space exploration. This thesis presents several novel and efficient techniques for soft error evaluation and mitigation at high abstract levels, i.e. from register transfer level up to behavioral algorithmic level. The effectiveness of proposed techniques is demonstrated with extensive synthesis experiments