FPGA HLS today: successes, challenges, and opportunities
The year 2011 marked an important transition for FPGA high-level synthesis (HLS), as it
went from prototyping to deployment. A decade later, in this article, we assess the progress …
went from prototyping to deployment. A decade later, in this article, we assess the progress …
[HTML][HTML] Computer vision algorithms and hardware implementations: A survey
X Feng, Y Jiang, X Yang, M Du, X Li - Integration, 2019 - Elsevier
The field of computer vision is experiencing a great-leap-forward development today. This
paper aims at providing a comprehensive survey of the recent progress on computer vision …
paper aims at providing a comprehensive survey of the recent progress on computer vision …
A survey on deep learning in medicine: Why, how and when?
New technologies are transforming medicine, and this revolution starts with data. Health
data, clinical images, genome sequences, data on prescribed therapies and results …
data, clinical images, genome sequences, data on prescribed therapies and results …
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 …
A configurable cloud-scale DNN processor for real-time AI
J Fowers, K Ovtcharov, M Papamichael… - 2018 ACM/IEEE 45th …, 2018 - ieeexplore.ieee.org
Interactive AI-powered services require low-latency evaluation of deep neural network
(DNN) models-aka"" real-time AI"". The growing demand for computationally expensive …
(DNN) models-aka"" real-time AI"". The growing demand for computationally expensive …
A survey of neuromorphic computing and neural networks in hardware
Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices,
and models that contrast the pervasive von Neumann computer architecture. This …
and models that contrast the pervasive von Neumann computer architecture. This …
Bit fusion: Bit-level dynamically composable architecture for accelerating deep neural network
Hardware acceleration of Deep Neural Networks (DNNs) aims to tame their enormous
compute intensity. Fully realizing the potential of acceleration in this domain requires …
compute intensity. Fully realizing the potential of acceleration in this domain requires …
Finn: A framework for fast, scalable binarized neural network inference
Research has shown that convolutional neural networks contain significant redundancy, and
high classification accuracy can be obtained even when weights and activations are …
high classification accuracy can be obtained even when weights and activations are …
A survey of FPGA-based accelerators for convolutional neural networks
S Mittal - Neural computing and applications, 2020 - Springer
Deep convolutional neural networks (CNNs) have recently shown very high accuracy in a
wide range of cognitive tasks, and due to this, they have received significant interest from the …
wide range of cognitive tasks, and due to this, they have received significant interest from the …
DNNBuilder: An automated tool for building high-performance DNN hardware accelerators for FPGAs
Building a high-performance FPGA accelerator for Deep Neural Networks (DNNs) often
requires RTL programming, hardware verification, and precise resource allocation, all of …
requires RTL programming, hardware verification, and precise resource allocation, all of …