In the real-time systems domain, time-randomised caches have been proposed as a way to simplify software timing analysis, i.e. the process of estimating the probabilistic worst case execution time (pWCET) of an application. However, the technology scaling of the cache memory manufacturing process is rendering transient and permanent faults more and more likely. These faults, in turn, affect a system's timing behaviour and the complexity of its analysis. In this study, the authors propose a static probabilistic timing analysis approach for time-randomised caches that is able to account for the presence of faults – and their detection mechanisms – using a state-space modelling technique. Their experiments show that the proposed methodology is capable of providing tight pWCET estimates. In their analysis, the effects on the estimation of safe pWCET bounds of two online mechanisms for the detection and classification of faults, i.e. a rule-based system and dynamic hidden Markov models (D-HMMs), are compared. The experimental results show that different mechanisms can greatly affect safe pWCET margins and that, by using D-HMMs, the pWCET of the system can be improved with respect to rule-based detection.

In this study, the authors propose a new group testing based (GTB) error control codes (ECCs) approach for improving the reliability of memory structures in computing systems. Compared with conventional single- and double-bit error correcting codes, the GTB codes provide higher reliability at the multi-byte error correction granularity. The proposed codes are cost-efficient in their encoding and decoding procedures. Instead of requiring multiplication or inversion over Galois finite field like most multi-byte ECC schemes, the proposed technique only involves bitwise XOR operations, therefore, significantly reducing the computation complexity and latency. For instance, to correct m errors in a Q-ary codeword of length N, where , the compute complexity is mere . The GTB codes trade redundancy for encoding and decoding simplicity, and are able to achieve better code rate than other ECCs of the same trade-off. The proposed GTB codes lend themselves well to designs with high reliability and low computation complexity requirements, such as storage systems with strong fault tolerance, or compute systems with straggler tolerance, and so on.

Field-programmable gate arrays are susceptible to radiation-induced single event upsets. These are commonly dealt with using triple modular redundancy (TMR) and module-based configuration memory error recovery (MER). By triplicating components and voting on their outputs, TMR helps localise configuration memory errors, and by reconfiguring faulty components, MER swiftly corrects them. However, the order in which TMR voters are checked inevitably impacts the overall system reliability. In this study, the authors outline an approach for computing the reliability of TMR–MER systems that consist of finitely many components. They demonstrate that system reliability is improved when the more vulnerable components are checked more frequently than when they are checked in round-robin order. They propose a genetic algorithm for finding a voter checking schedule that maximises the reliability of TMR–MER systems. Results indicate that the mean time to failure (MTTF) of these systems can be increased by up to 400% when variable-rate voter checking (VRVC) is used instead of round robin. They show that VRVC achieves 15–23% increase in MTTF with a 10× reduction in checking frequency to reduce system power. They also found that VRVC detects errors 44% faster on average than round robin.

Production of correct bioassay outcome is the foremost objective in digital microfluidic biochips (or DMFBs). In high-frequency DMFBs, continuous actuation of electrodes leads to malfunctioning or even breakdown of the system. The improper functioning of a biochip tends to produce erroneous results. On the other hand, while transporting droplets, the residues may get stuck to electrode walls and cause contamination to other droplets. To ensure proper assay outcome, washing becomes mandatory, whose incorporation may delay the bioassay completion time significantly. Furthermore, each wash droplet possesses a capacity constraint within which the residues can be washed off successfully. Evidently, the design objectives possess a large degree of trade-offs among themselves and must be attacked to prepare an efficient platform. Here, the authors propose a complete fluid-level synthesis considering all the essential goals together instead of dealing with them in isolation. The presented approach effectively handles the trade-off scenarios and provides flexibility to the designer to decide the threshold of the individual optimisation objective leading to the construction of a good-quality solution as a whole. The performance is evaluated over several benchmark bioassays.