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Hyperdimensional (HD) computing is a novel approach to machine learning inspired by neuroscience, which uses vectors in a hyper-dimensional space to represent data and models. This approach has gained significant interest in recent years with applications ...
Background and Objective: Cough audio signal classification is a potentially useful tool in screening for respiratory disorders, such as COVID-19. Since it is dangerous to collect data from patients with contagious diseases, many research teams have turned ...
This thesis demonstrates that it is feasible for systems code to expose a latency interface that describes its latency and related side effects for all inputs, just like the code's semantic interface describes its functionality and related side effects.Sem ...
The pervasive adoption of field-programmable gate arrays (FPGAs) in both cyber-physical systems and the cloud has raised many security issues. Being integrated circuits, FPGAs are susceptible to fault and power side-channel attacks, which require physical ...
The digital revolution has significantly transformed our world over the past decades, driven by the scaling of transistor dimensions and the exponential increase in computation power. However, as the CMOS scaling era approaches its end, the semiconductor i ...
With Moore's law coming to an end, increasingly more hope is being put in specialized hardware implemented on reconfigurable architectures such as Field-Programmable Gate Arrays (FPGAs). Yet, it is often neglected that these architectures themselves experi ...
Agent-based simulations have been widely applied in many disciplines, by scientists and engineers alike. Scientists use agent-based simulations to tackle global problems, including alleviating poverty, reducing violence, and predicting the impact of pandem ...
The crowdedness of the RF spectrum constitutes problems in communication, however it also accommodates large amount of useful information. This information can be used to determine existing signals and operating contexts. However, spectrum condition varies ...
State-of-the-art Artificial Intelligence (AI) algorithms, such as graph neural networks and recommendation systems, require floating-point computation of very large matrix multiplications over sparse data. Their execution in resource-constrained scenarios, ...
Dataflow programming is a methodology that enables the development of high-level, parametric programs that are independent of the underlying platform. This approach is particularly useful for heterogeneous platforms, as it eliminates the need to rewrite ap ...
