Convolutional neural networks (CNNs) have proven to be a disruptive technology in most
vision, speech and image processing tasks. Given their ubiquitous acceptance, the research …

Hardware acceleration of Deep Neural Networks (DNNs) aims to tame their enormous
compute intensity. Fully realizing the potential of acceleration in this domain requires …

Deep neural networks (DNN) have demonstrated highly promising results across computer
vision and speech recognition, and are becoming foundational for ubiquitous AI. The …

Deep Neural Networks (DNNs) are compute-intensive learning models with growing
applicability in a wide range of domains. FPGAs are an attractive choice for DNNs since they …

The data partitioning and scheduling strategies used by DNN accelerators to leverage reuse
and perform staging are known as dataflow, which directly impacts the performance and …

Convolutional neural networks (CNNs) are revolutionizing machine learning, but they
present significant computational challenges. Recently, many FPGA-based accelerators …

Deep convolutional neural networks (DCNNs) have been successfully used in many
computer vision tasks. Previous works on DCNN acceleration usually use a fixed …

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

Benefitting from large-scale training datasets and the complex training network,
Convolutional Neural Networks (CNNs) are widely applied in various fields with high …

Hybrid neural networks (hybrid-NNs) have been widely used and brought new challenges to
NN processors. Thinker is an energy efficient reconfigurable hybrid-NN processor fabricated …