Mixed-precision neural networks (MPNNs) that enable the use of just enough data width for
a deep learning task promise significant advantages of both inference accuracy and …

Quantization, effective Neural Network architecture, and efficient accelerator hardware are
three important design paradigms to maximize accuracy and efficiency. Mixed Precision …

The resource requirements of deep neural networks (DNNs) pose significant challenges to
their deployment on edge devices. Common approaches to address this issue are pruning …

Sparsification technology is crucial for deploying convolutional neural networks in resource-
constrained environments. However, the efficiency of sparse models is hampered by …

With the support of more precision formats in emerging hardware architectures, mixed-
precision has become a popular approach to accelerate deep learning (DL) training …

Deep Learning has achieved remarkable progress in the last decade due to its powerful
automatic representation capability for a variety of tasks, such as Image Recognition …