Synchronous dynamical systems are well-established models that have been used to capture a range of phenomena in networks, including opinion diffusion, spread of disease and product adoption. We study the three most notable problems in synchronous dynamical systems: whether the system will transition to a target configuration from a starting configuration, whether the system will reach convergence from a starting configuration, and whether the system is guaranteed to converge from every possible starting configuration. While all three problems were known to be intractable in the classical sense, we initiate the study of their exact boundaries of tractability from the perspective of structural parameters of the network by making use of the more fine-grained parameterized complexity paradigm. 

As our first result, we consider treewidth - as the most prominent and ubiquitous structural parameter - and show that all three problems remain intractable even on instances of constant treewidth. We complement this negative finding with fixed-parameter algorithms for the former two problems parameterized by treedepth, a well-studied restriction of treewidth. While it is possible to rule out a similar algorithm for convergence guarantee under treedepth, we conclude with a fixed-parameter algorithm for this last problem when parameterized by treedepth and the maximum in-degree.

A Structural Complexity Analysis of Synchronous Dynamical Systems

For p-type Cu2O thin film transistors (TFTs), high contact resistance is one of the critical issues that limit field effect mobility. Thus, to alleviate the contact resistance issue, in this work, we for the first time demonstrated Cu2O TFTs with nitrogen-doped Cu2O S/D interlayer. We observe a reduced contact resistance and improved field effect mobility when the N:Cu2O S/D interlayer is used.

N:Cu2O S/D for low contact resistance p-type Cu2O thin film transistor

Up-scaling of quantum information processors requires the cryogenic control CMOS devices to be operated in the few millivolt regime. However, MOSFETs show inverse subthreshold slope SS values which saturate well above the theoretical Boltzmann limit at cryogenic temperatures. In addition to this saturation of SS caused by band tailing, inflection degrades SS as a function of the gate voltage. We show gate stack engineering as an approach to counteract those phenomena which facilitate the realization of steep slope cryogenic MOSFETs.

Interface Engineering for Steep Slope Cryogenic MOSFETs

Performance Improvement and Reliability Physics in SiC MOSFETs

New Reliability Ecosystem: Maximize Technology Value to Serve Diverse Markets

Silicon Lifecycle Management: Increasing Semiconductor Predictability in an Unpredictable World

Transformation of Memory and Storage Hierarchy: Enabling high performance, reliable, and sustainable compute applications

Reliability at Scale: Pushing the Boundaries of Reliability and Quality at the Frontiers of Compute

Cyber-Physical Systems (CPS) are increasingly incorporating Learning-Enabled Components (LEC) to implement complex functions. By LEC we mean a component (typically, but not exclusively, implemented in software) that is realized with the help of data-driven techniques, like machine learning. For example, an LEC in an autonomous car can implement a lane follower function such that the developer trains an appropriate convolutional neural network with a stream of images of the road as input and the observed actions of a human driver as output. For high-consequence systems the challenge is to prove that the resulting system is safe: it does no harm, and it is ‘live’: it is functional. Safety is perhaps the foremost problem in autonomous vehicles, especially for ones that operate in a less-regulated environment, like the road network. The traditional approach for proving the safety of systems is based on extensively documented but often informal arguments – that are very hard to apply to CPS with LEC. The talk will focus on a current project that aims at changing this paradigm by introducing (1) formal verification techniques whenever possible (including proving properties of the ‘learned’ component), (2) monitoring technology for assurance to indicate when the LEC is not performing well, and (3) formalizing the safety case argumentation process so that it can be dynamically evaluated. The application target is autonomous vehicles. The goal is to construct a model-based engineering process and a supporting model-based toolchain that can be used for the engineering and systematic assurance of CPS with LECs.

Model-based Development and Assurance of Learning-enabled Cyber-Physical Systems

In mixed-criticality systems (MCSs), less-critical tasks are often terminated to ensure the correct execution of high critical tasks. This strategy could however lead to safety hazards, and largely reduce system functionality due to the absence of the discarded tasks. To overcome this problem, we introduce a high resilience and approximated computing framework for MCS, i.e., HIART-MCS. HIART-MCS introduces a novel processor which supports approximation at the hardware level. Associated with this, we also introduce a new intermediate system mode which allows less-critical tasks to be executed with reduced precision instead of being directly dropped out. Corresponding to the HIART-MCS, we further present a new theoretical model and schedulability analysis providing a timing guarantee for the system, followed by optimisations of the mode switch strategy. As demonstrated in both the theoretical and practical evaluations, HIART-MCS effectively improves the survivability of less-critical tasks with limited sacrifice of the critical tasks and negligible extra overhead. It is notable that HIART-MCS is the first practical framework for imprecise MCSs.

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A Structural Complexity Analysis of Synchronous Dynamical Systems

N:Cu2O S/D for low contact resistance p-type Cu2O thin film transistor

Interface Engineering for Steep Slope Cryogenic MOSFETs

Performance Improvement and Reliability Physics in SiC MOSFETs

New Reliability Ecosystem: Maximize Technology Value to Serve Diverse Markets

Silicon Lifecycle Management: Increasing Semiconductor Predictability in an Unpredictable World

Transformation of Memory and Storage Hierarchy: Enabling high performance, reliable, and sustainable compute applications

Reliability at Scale: Pushing the Boundaries of Reliability and Quality at the Frontiers of Compute

Model-based Development and Assurance of Learning-enabled Cyber-Physical Systems

HIART-MCS: High Resilience and Approximated Computing Architecture for Imprecise Mixed-Criticality Systems

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