Due to recent advances in digital technologies, and availability of credible data, an area of
artificial intelligence, deep learning, has emerged and has demonstrated its ability and …

In recent years, Convolutional Neural Networks (CNNs) have become widely adopted for
computer vision tasks. FPGAs have been adequately explored as a promising hardware …

Convolutional neural network (CNN) finds applications in a variety of computer vision
applications ranging from object recognition and detection to scene understanding owing to …

Dense matrix multiply (MM) serves as one of the most heavily used kernels in deep learning
applications. To cope with the high computation demands of these applications …

In recent years, convolutional neural networks (CNNs) have become widely adopted for
computer vision tasks. Field-programmable gate arrays (FPGAs) have been adequately …

Modern high-level synthesis (HLS) tools greatly reduce the turn-around time of designing
and implementing complex FPGA-based accelerators. They also expose various …

Deep neural networks (DNNs), as the basis of object detection, will play a key role in the
development of future autonomous systems with full autonomy. The autonomous systems …

FPGAs have been an efficient accelerator for CNN inference due to its high performance,
flexibility, and energy-efficiency. To improve the performance of CNNs on FPGAs, fast …

Surface codes reach high error thresholds when decoded with known algorithms, but the
decoding time will likely exceed the available time budget, especially for near-term …

Field-programmable gate arrays (FPGAs) provide an opportunity to co-design applications
with hardware accelerators, yet they remain difficult to program. High-level synthesis (HLS) …