The rapid growth of demanding applications in domains applying multimedia processing
and machine learning has marked a new era for edge and cloud computing. These …

The challenging deployment of compute-intensive applications from domains such as
Artificial Intelligence (AI) and Digital Signal Processing (DSP), forces the community of …

Approximate computing is an emerging design paradigm that enables highly efficient
hardware and software implementations by exploiting the inherent resilience of applications …

Diminishing benefits from technology scaling have pushed designers to look for new
sources of computing efficiency. Multicores and heterogeneous accelerator-based …

Many modern computations (such as video and audio encoders, Monte Carlo simulations,
and machine learning algorithms) are designed to trade off accuracy in return for increased …

Approximate computing leverages the intrinsic resilience of applications to inexactness in
their computations, to achieve a desirable trade-off between efficiency (performance or …

Emerging high-performance architectures are anticipated to contain unreliable components
that may exhibit soft errors, which silently corrupt the results of computations. Full detection …

As the size of datasets continues to grow, machine learning applications are becoming
increasingly limited by the amount of available computational power. Taking advantage of …

Approximate computing explores opportunities that emerge when applications can tolerate
error or inexactness. These applications, which range from multimedia processing to …

Computing systems are becoming increasingly parallel and heterogeneous, and therefore
new applications must be capable of exploiting parallelism in order to continue achieving …