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 …

Molecular dynamics (MD) simulation is a fundamental method for modeling ensembles of
particles. In this paper, we introduce a new method to improve the performance of MD by …

Deep neural networks (DNNs) with embedding layers are widely adopted to capture
complex relationships among entities within a dataset. Embedding layers aggregate multiple …

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

Driven by the demands of deep learning, many hardware accelerators, including GPUs,
have begun to include specialized tensor processing units to accelerate matrix operations …

With the increasing complexity of digital systems, managing power dissipation and energy
consumption in digital circuits, particularly in emerging embedded systems for artificial …

The computing industry is forced to find alternative design approaches and computing
platforms to sustain increased power efficiency, while providing sufficient performance …

Function memoization is an optimization technique that reduces a function call overhead
when the same input appears again. A table that stores the previous result is searched and …

Exploiting computational and data reuse in CNNs is crucial for the successful design of
resource-constrained platforms. In image recognition applications, high levels of input …

Approximate computing (AC) reduces power consumption and increases execution speed in
exchange for computational accuracy. By adjusting the accuracy of approximation at runtime …