The unprecedented progress in computational technologies led to a substantial proliferation
of artificial intelligence applications, notably in the era of big data and IoT devices. In the …

Libraries of approximate circuits are composed of fully characterized digital circuits that can
be used as building blocks of energy-efficient implementations of hardware accelerators …

Approximate circuits are digital circuits that are intentionally designed in such a way that the
specification is violated in terms of functionality in order to obtain some improvements in …

Approximate Computing (AxC) paradigm aims at designing computing systems that can
satisfy the rising performance demands and improve the energy efficiency. AxC exploits the …

Approximate Computing (AxC) has emerged as a promising paradigm to enhance
performance and energy efficiency by allowing a controlled trade-off between accuracy and …

As modern applications demand an unprecedented level of computational resources,
traditional computing system design paradigms are no longer adequate to guarantee …

As computational demand and energy efficiency of computer systems are becoming
increasingly relevant requirements, traditional design paradigms are bound to become no …

Graph Neural Networks (GNNs) are one of the best-performing models for processing graph
data. They are known to have considerable computational complexity, despite the smaller …

We have recently proposed approximate median filters (APMFs). They are based on the
sorting network and achieve acceptable image quality under low-cost hardware. In this brief …

Approximate circuits have been developed to provide good tradeoffs between power
consumption and quality of service in error resilient applications such as hardware …