FPGA-based accelerators of deep learning networks for learning and classification: A review
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 …
artificial intelligence, deep learning, has emerged and has demonstrated its ability and …
FPGA HLS today: successes, challenges, and opportunities
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 …
went from prototy** to deployment. A decade later, in this article, we assess the progress …
Hardware implementation of deep network accelerators towards healthcare and biomedical applications
The advent of dedicated Deep Learning (DL) accelerators and neuromorphic processors
has brought on new opportunities for applying both Deep and Spiking Neural Network …
has brought on new opportunities for applying both Deep and Spiking Neural Network …
A survey and evaluation of FPGA high-level synthesis tools
High-level synthesis (HLS) is increasingly popular for the design of high-performance and
energy-efficient heterogeneous systems, shortening time-to-market and addressing today's …
energy-efficient heterogeneous systems, shortening time-to-market and addressing today's …
Accelerating binarized convolutional neural networks with software-programmable FPGAs
Convolutional neural networks (CNN) are the current stateof-the-art for many computer
vision tasks. CNNs outperform older methods in accuracy, but require vast amounts of …
vision tasks. CNNs outperform older methods in accuracy, but require vast amounts of …
A survey of coarse-grained reconfigurable architecture and design: Taxonomy, challenges, and applications
As general-purpose processors have hit the power wall and chip fabrication cost escalates
alarmingly, coarse-grained reconfigurable architectures (CGRAs) are attracting increasing …
alarmingly, coarse-grained reconfigurable architectures (CGRAs) are attracting increasing …
Flextensor: An automatic schedule exploration and optimization framework for tensor computation on heterogeneous system
Tensor computation plays a paramount role in a broad range of domains, including machine
learning, data analytics, and scientific computing. The wide adoption of tensor computation …
learning, data analytics, and scientific computing. The wide adoption of tensor computation …
Three ages of FPGAs: a retrospective on the first thirty years of FPGA technology: this paper reflects on how Moore's law has driven the design of FPGAs through three …
SMS Trimberger - IEEE Solid-State Circuits Magazine, 2018 - ieeexplore.ieee.org
Since their introduction, field programmable gate arrays (FPGAs) have grown in capacity by
more than a factor of 10 000 and in performance by a factor of 100. Cost and energy per …
more than a factor of 10 000 and in performance by a factor of 100. Cost and energy per …
Evaluating fast algorithms for convolutional neural networks on FPGAs
In recent years, Convolutional Neural Networks (CNNs) have become widely adopted for
computer vision tasks. FPGAs have been adequately explored as a promising hardware …
computer vision tasks. FPGAs have been adequately explored as a promising hardware …
Large-scale MIMO detection for 3GPP LTE: Algorithms and FPGA implementations
Large-scale (or massive) multiple-input multiple-out put (MIMO) is expected to be one of the
key technologies in next-generation multi-user cellular systems based on the upcoming …
key technologies in next-generation multi-user cellular systems based on the upcoming …