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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. 

Triple modular redundancy (TMR) is commonly employed to increase the reliability and mean time to failure (MTTF) of a system. This improvement can be shown by using a continuous time Markov chain. However, typical Markov chain models do not model common cause failures (CCF), which is a singular event that simultaneously causes failure in multiple redundant modules. This paper introduces a new Markov chain to model CCF in TMR with repair systems. This new model is compared to the idealized models of TMR with repair without CCF. The fundamental limitations that CCF imposes on the system are shown and discussed. In a motivating example, it is seen that CCF imposes a limitation of 51× on the reliability improvement in a system with TMR and repair compared to a simplex system, (i.e., without TMR). A case study is also presented where the likelihood of CCF is reduced by a factor of 18× using various mitigation techniques. Reducing the CCF compounds the reliability improvement of TMR with repair and leads to a overall system reliability improvement of 10,000× compared to the simplex system as supported by the proposed model. 

FPGAs are being used in large numbers within cloud computing to provide high-performance, low-power alternatives to more traditional computing structures. While FPGAs provide a number of important benefits to cloud computing environments, they are susceptible to radiation-induced soft errors, which can lead to silent data corruption or system instability. Although soft errors within a single FPGA occur infrequently, soft errors in large-scale FPGAs systems can occur at a relatively high rate. This paper investigates the failure rate of several FPGA applications running within an FPGA cloud computing node by performing fault injection experiments to determine the susceptibility of these applications to soft-errors. The results from these experiments suggest that silent data corruption will occur every few hours within a 100,000 node FPGA system and that such a system can only maintain high-levels of reliability for short periods of operation. These results suggest that soft-error detection and mitigation techniques may be needed in large-scale FPGA systems. 

TMR combined with configuration scrubbing is an effective technique to mitigate against radiation-induced CRAM upsets on SRAM-based FPGAs. However, its effectiveness is limited by low-level common mode failures due to the physical mapping of a design to the FPGA device. This paper describes how common mode failures are introduced during the implementation process and introduces an approach for resolving them through a custom incremental placement tool for Xilinx 7-Series FPGAs. Multiple designs across multiple generations of devices are shown to be sensitive to common mode failures. Applying the incremental placement technique yields an improvement of 10,721x over an unmitigated design through fault-injection testing. Radiation testing is then performed to show that the of this technique is 91,500 days in GEO orbit, a 367x improvement over the unmitigated design and a 5x improvement over baseline TMR. 

Abstract Embedding tunable quantum emitters in a photonic bandgap structure enables control of dissipative and dispersive interactions between emitters and their photonic bath. Operation in the transmission band, outside the gap, allows for studying waveguide quantum electrodynamics in the slow-light regime. Alternatively, tuning the emitter into the bandgap results in finite-range emitter–emitter interactions via bound photonic states. Here, we couple a transmon qubit to a superconducting metamaterial with a deep sub-wavelength lattice constant (λ/60). The metamaterial is formed by periodically loading a transmission line with compact, low-loss, low-disorder lumped-element microwave resonators. Tuning the qubit frequency in the vicinity of a band-edge with a group index ofn_g = 450, we observe an anomalous Lamb shift of −28 MHz accompanied by a 24-fold enhancement in the qubit lifetime. In addition, we demonstrate selective enhancement and inhibition of spontaneous emission of different transmon transitions, which provide simultaneous access to short-lived radiatively damped and long-lived metastable qubit states.