SRAM-based Field Programmable Gate Arrays Research Articles

A Review on Soft Error Correcting Techniques of Aerospace-Grade Static RAM-Based Field-Programmable Gate Arrays.

Aerospace-grade SRAM-based field-programmable gate arrays (FPGAs) used in space applications are highly susceptible to single event effects, leading to soft errors in FPGAs. Additionally, as FPGAs scale up, the difficulty of correcting soft errors also increases. This paper proposes that performing soft error sensitivity analysis on FPGAs can help target the more sensitive areas for detection and correction, thereby improving the efficiency of soft error repair. Firstly, in accordance with the dual-layer architecture of SRAM-based FPGAs, methods for the soft error sensitivity analysis of FPGA application layer resources and configuration bitstreams are reviewed. Subsequently, based on the analysis results, it also covers corresponding application layer memory scrubbing and configuration scrubbing techniques. A prospective look at emerging soft error mitigation technologies is discussed at the end of this review, supporting the development of highly reliable aerospace-grade SRAM-based FPGAs.

A Hierarchical Priority Based Multi-Bit Error Tolerant Design for FPGA Configuration Memory

The reconfigurability of a field-programmable gate array (FPGA), which is utilized in many applications, permits changes to be made to an existing design. In hostile environments such as space, where radiation and ionizing particles increase the risk for failure and human maintenance is either impossible or time-consuming, this reconfigurable capability becomes even more important. Single event upsets (SEUs) and multi-bit upsets (MBUs) on configuration memory can cause soft errors and circuit malfunctions in SRAM-based field programmable gate arrays (FPGAs). The ever-increasing quantity of configuration bits in contemporary FPGAs makes it more difficult for traditional scrubbing to identify faults in a timely manner, leading to a mismatch between scrubbing performance and MBU sensitivity.A hierarchical multi-bit error correcting method fully utilizes the MBU sensitivity at various configuration frame locations. It employs distinct techniques for varying priorities and differentiates across configuration frames with multiple priorities. They divided code words into several sections and used distinct error-correction techniques in each section. In high radiation conditions, the suggested integrated error correction technique enhances FPGA configuration memory protection by substituting memory scrubbing. The suggested approach lowers total TTD and enables priority-based asynchronous MBU detection and correction. Index Terms—FPGA,MBU,Sensitivity,Configuration memory

Proton irradiation of FPGA-embedded hadron fluence sensors

Hadron fluence monitors based on static random access memories (SRAMs) are in use at particle accelerators for the protection of electronics and at proton-therapy facilities for measuring secondary neutrons. One shortcoming of current solutions is that they necessitate SRAMs to detect radiation-induced single event upsets and require additional components to transfer the counts from the radiation area. This approach limits the practicality of the sensors due to their power requirements, increased board complexity, larger physical footprint and reduced total ionizing dose tolerance. In this work, we present proton irradiation test results of a novel proton and neutron fluence sensor we designed. The sensor is fully embedded in a SRAM-based field programmable gate array (FPGA) solving the abovementioned limitations of the state of the art. The FPGA configuration SRAM is used as a sensitive element, while the Joint Test Action Group interface is leveraged as a read out circuitry. We irradiated six sensor units by means of a variable-energy proton beam at the Trento Institute for Fundamental Physics and Applications (TIFPA, Trento, Italy). We discuss our results in terms of proton to upset cross section as a function of the proton energy in the range from 70 to 228 MeV, also parameterized with respect to the power supply voltage. We evaluate the variability of the cross section in the set of irradiated samples. Moreover, we show how we used the device to image the proton beam and measure beam profiles for aligning the configuration RAM and beam centers. We also suggest alternate reading modes to measure the fluence on the device by measuring the drawn current, rather than counting upsets.

Comparative study: AutoDPR-SEM for enhancing CNN reliability in SRAM-based FPGAs through autonomous reconfiguration

Convolutional neural networks (CNNs) are widely adopted in safety-critical systems, including space applications and autonomous vehicles. Field-programmable gate arrays (FPGAs) based on SRAM are preferred for accelerating CNN computations due to their unique characteristics. However, the configuration memory of FPGAs is susceptible to single event effects (SEEs), which can corrupt computations and lead to misclassification of CNN outputs. In this study, we investigated the impact of SEEs on SRAM-based FPGAs with Two-Photon Absorption (TPA) laser fault injections through a comparative analysis of two popular CNN acceleration architectures: streaming architecture (SA) and single compute engine (SCE). Experimental results show that SA-based CNNs require more hardware resources but exhibit superior resilience against single event upsets (SEUs). Without any Radiation Hardened by Design (RHBD) protection, SCE has an error rate approximately twice as high as SA. To mitigate errors, the Xilinx IP core - Soft Error Mitigation (SEM) is used for error detection and correction, leading to error rate reductions of up to 50 % in both architectures. Importantly, we propose the AutoDPR-SEM (Autonomous Dynamic Partial Reconfiguration for Soft Error Mitigation) approach, which automatically reconfigures the SEM IP core when it remains idle due to uncorrectable errors. AutoDPR-SEM significantly improves CNN error rates, reducing errors by approximately 17.8 times in SCE and 14.8 times in SA. We also applied software level simulation to validate the TPA experiment, showing similar trends of the testing results across all models. In conclusion, the study confirms the feasibility of AutoDPR-SEM in both architectures, showcasing its potential to improve CNN error rates in safety-critical systems.

Recent advances on reliability of FPGAs in a radiation environment

In recent years, SRAM-based Field-Programmable Gate Arrays (FPGAs) have seen a surge in deployment within aerospace applications. Despite their widespread use, these FPGAs, particularly their embedded Static Random Access Memory (SRAM) and user logic, exhibit a notable susceptibility to Single Event Upsets (SEU), a phenomenon leading to erroneous connections or routing discrepancies. This paper presents an exhaustive survey of fault-tolerance methodologies pertinent to SRAM-based FPGAs operating in radiation-intensive environments. Initially, we delineate the architecture of the SRAM-based FPGAs, elucidating the mechanisms underlying SEU occurrences and their subsequent malfunctions. This is followed by a detailed exploration of various strategies employed to assess the resilience of SRAM-based FPGAs against SEU-related disruptions, mainly including the Triple Module Redundancy (TMR) and the configuration scrubbing technology. Collectively, this survey aspires to function as an instructive compendium for engineers and researchers specializing in the development of fault-tolerance mechanisms for SRAM-based FPGAs, particularly in the context of aerospace applications.

Implementation of Highly Reliable Convolutional Neural Network with Low Overhead on Field-Programmable Gate Array

Due to the advantages of parallel architecture and low power consumption, a field-programmable gate array (FPGA) is typically utilized as the hardware for convolutional neural network (CNN) accelerators. However, SRAM-based FPGA devices are extremely susceptible to single-event upsets (SEUs) induced by space radiation. In this paper, a fault tolerance analysis and fault injection experiments are applied to a CNN accelerator, and the overall results show that SEUs occurring in a control unit (CTRL) lead to the highest system error rate, which is over 70%. After that, a hybrid hardening strategy consisting of a finite state machine error-correcting circuit (FSM-ECC) and a triple modular redundancy automatic hardening technique (TMR-AHT) is proposed in this paper to achieve a tradeoff between radiation reliability and design overhead. Moreover, the proposed methodology has very small workload and good migration ability. Finally, by full exploiting the fault tolerance property of CNNs, a highly reliable CNN accelerator with the proposed hybrid hardening strategy is implemented with Xilinx Zynq-7035. When BER is 2 × 10−6, the proposed hybrid hardening strategy reduces the whole system error rate by 78.95% with the overhead of an extra 20.7% of look-up tables (LUTs) and 20.9% of flip-flops (FFs).

Soft error mitigation and recovery of SRAM-based FPGAs using brain-inspired hybrid-grained scrubbing mechanism.

Soft error has increasingly become a critical concern for SRAM-based field programmable gate arrays (FPGAs), which could corrupt the configuration memory that stores configuration data describing the custom-designed circuit architecture. To mitigate this kind of error, this study proposes a brain-inspired hybrid-grained scrubbing mechanism consisting of fine-grained and coarse-grained scrubbing to mitigate and repair the errors as quickly as possible after an SEU occurrence. Inspired by the human brain's ability to filter out redundant and irrelevant information, we propose a mechanism that can mask invalid position information when errors occur. Compared with the scrubbing of full configuration memory, this mechanism can achieve precise error location and recovery utilizing targeted scrubbing of specific frames or modules. The effectiveness is evaluated by executing fault injection campaigns on the International Symposium on Circuits and Systems 1989 (ISCAS89) benchmark circuits and fault tolerant fast Fourier transform (FT-FFT) circuit. If upsets are detected, they will be repaired with fine-grained or coarse-grained scrubbing depending on their location. The experiment results show that this mechanism can effectively mitigate and repair single-bit upsets (SBUs) and double-bit upsets (DBUs). In addition, the mechanism is proven to be superior in error recovery time and hardware overhead compared to counterpart approaches.

Efficient Protection of FPGA Implemented LDPC Decoders Against Single Event Upsets (SEUs) on Configuration Memories

Low Density Parity Check (LDPC) codes are used in 5G systems for traffic channels due to their excellent error correction capability for long sequences, and the Min-Sum algorithm is widely applied in practical implementations of LDPC decoders due to its low complexity. If the decoder is implemented on a SRAM-based field-programmable gate array (SRAM-FPGA), the radiation-induced single-event upsets (SEUs) can affect the operation of the LDPC decoder by corrupting the configuration memory, which can change the circuit functionality and will not be corrected unless the FPGA is reconfigured. Therefore, protection of LDPC decoders with low overhead is an important problem, especially for resource-limited on-board space systems. In this paper, an efficient Duplicate With Comparison (DWC) protection scheme is proposed based on the different distribution of the parity check sum of the LDPC decoder in the error-free case and the faulty case. In particular, the check sum accumulation number and threshold are optimized to achieve high detection probability with short delay. FPGA based implementation and hardware fault injection experiments are conducted to evaluate the performance of the proposed schemes. Experimental results show that, the effect of SEUs on the LDPC decoder can be completely eliminated by the proposed scheme with 2 times computational overhead and 1.69 times power consumption overhead compared to the unprotected decoder.

Toward Optimal Softcore Carry-aware Approximate Multipliers on Xilinx FPGAs

Domain-specific accelerators for signal processing, image processing, and machine learning are increasingly being implemented on SRAM-based field-programmable gate arrays (FPGAs). Owing to the inherent error tolerance of such applications, approximate arithmetic operations, in particular, the design of approximate multipliers, have become an important research problem. Truncation of lower bits is a widely used approximation approach; however, analyzing and limiting the effects of carry-propagation due to this approximation has not been explored in detail yet. In this article, an optimized carry-aware approximate radix-4 Booth multiplier design is presented that leverages the built-in slice look-up tables (LUTs) and carry-chain resources in a novel configuration. The proposed multiplier simplifies the computation of the upper and lower bits and provides significant benefits in terms of FPGA resource usage (LUTs saving 38.5%–42.9%), Power Delay Product (PDP saving 49.4%–53%), performance metric (LUTs × critical path delay (CPD) × PDP saving 68.9%–73.1%) and errors (70% improvement in mean relative error distance) compared to the latest state-of-the-art designs. Therefore, the proposed designs are an attractive choice to implement multiplication on FPGA-based accelerators.

Neutron-Irradiation Testing of FPGA-Embedded Hadron Fluence Sensors

Hadron fluence monitors based on static random access memories (SRAMs) are being used at CERN and have been proposed for proton therapy facilities. Some of the limitations of the state of the art are related to the usage of separate components for sensing upsets and for reading them out, as these increase the power consumption, the board complexity and its size. Moreover, in some cases, due to radiation-tolerance requirements, the readout logic is fixed and it cannot be updated once the system has been implemented. In this work, we show how to overcome the mentioned limitations by using an SRAM-based field programmable gate array (FPGA) for implementing both the sensitive element (the configuration SRAM) and the readout logic (the firmware in the fabric). In fact, we describe the implementation of a compact, reprogrammable, low-power, actively self-reading hadron fluence sensor realized by means of a Xilinx Artix-7 FPGA. Moreover, we present the customized radiation-hardening-by-design techniques adopted for the readout logic. We irradiated two sensor prototypes at the Jožef Stefan Institute’s TRIGA Mark II research reactor, under different neutron spectra and flux conditions. We discuss our results, which include the measurements of the radiation tolerance, the single event upset cross section of the configuration SRAM, the sensitivity to thermal neutrons, and the failure cross section of the readout logic.

Soft Error Sensitivity Analysis Based on 40 nm SRAM-Based FPGA

Soft errors induced by radiation are the major reliability threat for SRAM-based field-programmable gate arrays (FPGAs). A more detailed analysis of the soft error sensitivity of the 40 nm SRAM-based FPGA was performed. Experimental methods for the configurable logic module, configure memory cells, and block RAM have been introduced for measuring the single event effects (SEEs) induced by alpha particles using a 241Am radiation source. The single event upset (SEU) and single event functional interrupt (SEFI) cross sections of different functional blocks have been calculated to discuss the failure mechanisms of the FPGA. The SEEs test results for the FPGA device based on the 40 nm CMOS process are significant.

STT-MRAM-Based Multicontext FPGA for Multithreading Computing Environment

The demand for high-performance computing and rapidly increasing power consumption has increased the necessity for application-specific accelerators. In the datacenter and mobile system, more applications are increasingly relying on accelerators. Field-programmable gate arrays (FPGAs) emerge as a good candidate because they have high programmability and power efficiency. As the number of applications requiring acceleration increases, there is huge demand for FPGAs that support multiple contexts. Previous FPGA designs that support multicontext have various shortcomings such as volatility, poor power efficiency, large performance, area, and reconfiguration overhead. In this article, we propose a spin-transfer torque magnetic RAM (STT-MRAM)-based nonvolatile multicontext FPGA (NVMC-FPGA) that overcomes these shortcomings. We introduce the NVMC-FPGA architecture and operation modes that take advantage of nonvolatility and support multicontext. We also develop the multicontext-aware FPGA computer aided design flow to make the most of the NVMC-FPGA. Compared to the conventional SRAM-based FPGA, when eight identical circuits are mapped, the NVMC-FPGA improves the performance by 15.3% on average and reduces the power consumption by 11.2%–80.7%, depending on the number of simultaneously activated circuits. Moreover, when eight different circuits are mapped, the NVMC-FPGA improves the performance by 58.5% on average and reduces the power consumption by 6.2%–63.3%, depending on the number of simultaneously activated circuits.

Configuration Memory Scrubbing of SRAM-Based FPGAs Using a Mixed 2-D Coding Technique

SRAM-based field-programmable gate array (FPGA) vendors typically integrate error correction codes (ECCs) into the configuration memory to assist designers in implementing scrubbing mechanisms. In most cases, these ECC schemes guarantee the correction of single- and double-bit errors per configuration frame but fail to correct upsets with higher multiplicity in a single frame caused by a single event. This phenomenon has been observed in modern commercial-off-the-shelf FPGAs. Bit interleaving schemes are used in some FPGA families to scatter the multiple upsets into more than one frame, but this does not fully resolve the problem of uncorrectable errors. In this article, we propose a configuration memory scrubbing approach for SRAM-based FPGA devices, which combines the embedded ECC logic with an interframe, interleaved parity code to build a mixed 2-D coding technique. The proposed technique improves the multiple-bit error correction capabilities of the on-chip ECC scheme while keeping the error correction latency and hardware cost low. The scrubbing concept has been validated under heavy-ion irradiation, where it succeeded in correcting all the single and multiple upsets observed during the radiation experiment.

ACME-2: Improving the Extraction of Essential Bits in Xilinx SRAM-Based FPGAs

Fault emulation in field-programmable gate arrays (FPGAs) is a popular alternative to test the reliability of a design due to its low cost and high availability compared to traditional radiation-based approaches. Fault emulation is an instrumentation method based on inducing artificial bit flips on the design under test (DUT) to modify the content of its memory elements or the structure of the design itself. A major limitation of error emulation in FPGAs lies in the determination of the proper bits to test, which is directly related to the reliability results obtained and the execution times of the fault injection campaign. The Automatic Configuration Memory Error-injection (ACME) tool helps with this process in Xilinx FPGAs by obtaining the essential bits of an FPGA region where the DUT is. In this paper, we present ACME-2, an improved version of the tool where the translation from the FPGA region to design essential bits has been modified with architectural information of the FPGA. In this revision of the tool, redundant input/output routing bits that distort the reliability results are minimized. For the example designs used in the experiments, these modifications have led to an improvement in the precision of the reliability results as well as a reduction of the fault injection campaign runtime over the previous version of the tool.

AM&FT: An Aging Mitigation and Fault Tolerance Framework for SRAM-Based FPGA in Space Applications

Field programmable gate arrays (FPGAs) have become very widely used devices in space applications, and their runtime reconfigurable architecture allows for the area and power acceleration for complex applications. However, FPGAs are increasingly susceptible to aging effects and failures due to harsh space environments and long operation cycles, which reduce the reliability and lifetime of such devices. Although offline aging-aware layout-based methods are effective in aging mitigation, existing studies ignore the fault tolerance needs of the task and the layout strategy will completely fail after a hard failure occurs. This paper presents a reliability framework AM&FT for SRAM-based FPGAs in space applications to support on-chip aging mitigation and fault tolerance. We use an Integer Linear Programming (ILP) model to solve mapping relationships between tasks and reconfigurable blocks (Rbs) in the offline phase to achieve the aging and reliability-aware layout strategy. Second, the ILP model is incorporated into the Design Space Exploration (DSE) to generate a set of layout strategies to tolerate hard faults. Moreover, the state model is used for runtime fault management to handle the impact of different types of faults on the device. Experimental results demonstrate that our framework achieves FPGA on-chip aging mitigation and fault tolerance. Compared with the existing methods, AM&FT can guarantee the fault tolerance requirements of tasks and give priority to guarantee the Quality of Service (QoS) of critical tasks under the condition of hard faults accumulation. In addition, our framework delivers up to [Formula: see text] mean time to failure (MTTF) than the baseline.

Low-Cost Strategy to Detect Faults Affecting Scrubbers in SRAM-Based FPGAs

SRAM-based Field Programmable Gate Arrays (FPGAs) are vulnerable to SEUs. For applications demanding high reliability this problem is often solved by integrating in the system a scrubber, a circuit that periodically scans the FPGA configuration memory and reconfigures it if an error is detected. Since the scrubber is usually implemented in the same FPGA device, it is also vulnerable to SEUs, thus the scrubber reliability is increased by adopting standard fault tolerance techniques. These solutions guarantee the scrubber reliability, but generally require a large area overhead.In this paper, we present a novel low-cost strategy capable to detect faults in the FPGA configuration memory implementing the scrubber. The proposed technique is based on time redundancy, forcing the scrubber output to produce an error indication for each word read from the FPGA memory, in order to detect the faults affecting the portion of FPGA memory implementing the scrubber. The implementation of our proposed strategy presents a negligible impact in terms of area overhead (4.17%) and a limited increase in power consumption (22.9%) over the original (unprotected) scrubber. As for the impact on system performance introduced by our strategy, it is of approximately the 38.2% over the unprotected scrubber, but it can be significantly lowered by reducing the frequency at which the scrubber is applied to test the FPGA.

The Impact of Terrestrial Radiation on FPGAs in Data Centers

Field programmable gate arrays (FPGAs) are used in large numbers in data centers around the world. They are used for cloud computing and computer networking. The most common type of FPGA used in data centers are re-programmable SRAM-based FPGAs. These devices offer potential performance and power consumption savings. A single device also carries a small susceptibility to radiation-induced soft errors, which can lead to unexpected behavior. This article examines the impact of terrestrial radiation on FPGAs in data centers. Results from artificial fault injection and accelerated radiation testing on several data-center-like FPGA applications are compared. A new fault injection scheme provides results that are more similar to radiation testing. Silent data corruption (SDC) is the most commonly observed failure mode followed by FPGA unavailable and host unresponsive. A hypothetical deployment of 100,000 FPGAs in Denver, Colorado, will experience upsets in configuration memory every half-hour on average and SDC failures every 0.5–11 days on average.

Ultralow Power System-on-Chip SRAM Characterization by Alpha and Neutron Irradiation

The static random access memory (SRAM) of an ultralow power system-on-chip (SoC) was tested for single-event upsets (SEUs) using alpha particles and neutron beam sources. The measurements are compared to those of an SRAM-based field-programmable gate array (FPGA), built on a similar technology node. The results reveal opposite trends in the two devices regarding the upsets of the logic states, as well as differences in the dependence of SEU cross section (CS) on the operation voltage. The sensitivity of the SoC SRAM to multiple bit upsets with the different radiation sources is analyzed as well. The results demonstrate that the unique SoC design, which enables complete near/subthreshold operation, does not compromise the SoC bit upset tolerance compared to devices of similar technology node which operate at higher voltages.

Design and verification of multiple SEU mitigated circuits on SRAM-based FPGA system

This paper addresses the issue of soft error mitigation for Static Random-Access Memory (SRAM)-based Field Programmable Gate Arrays (FPGAs) system in radiation environment to reduce their malfunction and system failure rates in space missions. Seven representative circuits are designed by using the logical resources of SRAM-based FPGA. The accelerator tests investigate that the clock distribution of the Triple Modular Redundancy (TMR) circuits is vital to achieve a high Single Event Upset (SEU) tolerance. The separated DTMR_NEW circuits are proposed to overcome the weakness of the conventional TMR circuits, and a 25× improvement of SEU tolerance for the separated DTMR_NEW circuits are verified. The statistical estimations are desirable for engineers to assess and enhance the SEU tolerance of their designed systems at earlier stages to reduce the time and development costs.

Identifying Radiation-Induced Micro-SEFIs in SRAM FPGAs

Field-programmable gate arrays (FPGAs) are susceptible to radiation-induced effects that can affect more than one memory cell. Radiation-induced microsingle event functional interrupts (micro-SEFIs) are one of such events that can upset several bits at a time. These events need to be studied because they can overcome protection from techniques such as triple modular redundancy (TMR) and error correction codes (ECCs). Extracting these events from radiation data helps to understand if specific resources of the FPGA are more vulnerable and the extent of this vulnerability. This article presents a method based on statistics and fault injection to identify micro-SEFIs from beam-test data in the configuration memory and block RAM (BRAM) of SRAM-based FPGAs. The results show the cross section of these events for the configuration RAM (CRAM) and BRAM for three families of Xilinx SRAM FPGAs gathered throughout three neutron tests. This article also contains data from a fault injection campaign to uncover the possible CRAM source bits causing micro-SEFIs in memory look-up tables (LUTs) of Xilinx 7-series and Ultrascale devices.