An indispensable part of our personal and working lives, computing has also become
essential to industries and governments. Steady improvements in computer hardware have …

Federated Learning (FL) enhances predictive accuracy in load forecasting by integrating
data from distributed load networks while ensuring data privacy. However, the …

Approximate functional unit designs have the potential to reduce power consumption
significantly compared to their precise counterparts; however, few works have investigated …

Federated Learning (FL) in load forecasting improves predictive accuracy by leveraging
data from distributed load networks while preserving data privacy. However, the …

Transistor count continues to increase for silicon devices following Moore's Law. But the
failure of Dennard scaling has brought the computing community to a crossroad where …

Optimized deep neural network (DNN) models and energy-efficient hardware designs are of
great importance in edge-computing applications. The neural architecture search (NAS) …

Convolutional neural networks (CNNs) have been widely utilized in modern artificial
intelligent (AI) systems. In particular, GoogLeNet, one of the most popular CNNs, consisting …

As processors continue to get exponentially cheaper for end users following Moore's law,
the costs involved in their design keep growing, also at an exponential rate. The reason is …

Technologies are generally described for methods and systems effective to implement a
memory allocation accelerator. A processor may generate a request for allocation of a …

Processor specialization through application-specific instruction set customization can
significantly improve performance while reducing energy. Due to the costs associated with …