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

Approximate computing can significantly reduce the energy consumption of computing
systems. Mixed-precision hardware architectures and precision-tuning tools for software …

Background: Accurate and fast image registration (IR) is critical during surgical interventions
where the ultrasound (US) modality is used for image-guided intervention. Convolutional …

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

Mixed precision is an approximate computing technique that can be used to trade-off
computation accuracy for performance and/or energy. It can be applied to many error …

In the last decades, machine learning techniques have been extensively used in place of
classical template attacks to implement profiled side-channel analysis. This manuscript …

The development of intelligent measurement systems by implementing AI (artificial
intelligence) algorithms on edge sensor nodes is very topical. Due to very limited hardware …

Posit arithmetic has attracted a lot of attention as a promising alternative to the IEEE754
floating-point number representation thanks to its advantages such as higher accuracy and …

Attacks based on side-channel analysis (SCA) pose a severe security threat to modern
computing platforms, further exacerbated on IoT devices by their pervasiveness and …

The 32-bit floating-point (FP32) binary format, commonly used for data representation in
computers, introduces high complexity, requiring powerful and expensive hardware for data …