Understanding deep learning is increasingly emergent as it penetrates more and more into
industry and science. In recent years, a research line from Fourier analysis sheds light on …

Neural network quantization is a promising compression technique to reduce memory
footprint and save energy consumption, potentially leading to real-time inference. However …

Federated learning (FL) over mobile devices has fostered numerous intriguing
applications/services, many of which are delay-sensitive. In this paper, we propose a service …

Modern applications of neural-network-based AI solutions tend to move from datacenter
backends to low-power edge devices. Environmental, computational, and power constraints …

The recent breakthroughs of deep neural networks (DNNs) and the advent of billions of
Internet of Things (IoT) devices have excited an explosive demand for intelligent IoT devices …

Energy efficiency is essential for federated learning (FL) over mobile devices and its
potential prosperous applications. Different from existing communication efficient FL …

Fused Multiply-Add (FMA) functional units constitute a fundamental hardware component to
train Deep Neural Networks (DNNs). Its silicon area grows quadratically with the mantissa …

Vehicular edge intelligence (VEI) is a promising paradigm for enabling future intelligent
transportation systems by accommodating artificial intelligence (AI) at the vehicular edge …

Providing high-quality predictions is no longer the sole goal for neural networks. As we live
in an increasingly interconnected world, these models need to match the constraints of …

The unprecedented growth in DNN model complexity, size, and amount of training data has
led to a commensurate increase in demand for computing and a search for minimal …