Successful integration of deep neural networks (DNNs) or deep learning (DL) has resulted
in breakthroughs in many areas. However, deploying these highly accurate models for data …

The year 2011 marked an important transition for FPGA high-level synthesis (HLS), as it
went from prototy** to deployment. A decade later, in this article, we assess the progress …

Dynamic neural network is an emerging research topic in deep learning. Compared to static
models which have fixed computational graphs and parameters at the inference stage …

LiDAR point-cloud segmentation is an important problem for many applications. For large-
scale point cloud segmentation, the de facto method is to project a 3D point cloud to get a …

Designing accurate and efficient ConvNets for mobile devices is challenging because the
design space is combinatorially large. Due to this, previous neural architecture search (NAS) …

Abstract Model size and inference speed/power have become a major challenge in the
deployment of neural networks for many applications. A promising approach to address …

Abstract The best performing Binary Neural Networks (BNNs) are usually attained using
Adam optimization and its multi-step training variants. However, to the best of our …

Modern pre-trained transformers have rapidly advanced the state-of-the-art in machine
learning, but have also grown in parameters and computational complexity, making them …

Deep Neural Networks (DNNs) have achieved extraordinary performance in various
application domains. To support diverse DNN models, efficient implementations of DNN …

Binary neural networks (BNNs) have 1-bit weights and activations. Such networks are well
suited for FPGAs, as their dominant computations are bitwise arithmetic and the memory …