FPGA-based convolutional neural network architecture with reduced parameter requirements
The success of deep learning has fast paced the evolution of current technology at
unprecedented rate. In particular, deep convolutional neural networks (CNNs) has gained a …
unprecedented rate. In particular, deep convolutional neural networks (CNNs) has gained a …
Going deeper with embedded FPGA platform for convolutional neural network
In recent years, convolutional neural network (CNN) based methods have achieved great
success in a large number of applications and have been among the most powerful and …
success in a large number of applications and have been among the most powerful and …
Edgenet: Squeezenet like convolution neural network on embedded fpga
K Pradeep, K Kamalavasan… - 2018 25th IEEE …, 2018 - ieeexplore.ieee.org
In recent years, Convolution Neural Network (CNN) gained great success in many
applications, especially in computer vision. Now adapting CNN inference on edge devices …
applications, especially in computer vision. Now adapting CNN inference on edge devices …
MALOC: A fully pipelined FPGA accelerator for convolutional neural networks with all layers mapped on chip
Recently, field-programmable gate arrays (FPGAs) have been widely used in the
implementations of hardware accelerator for convolutional neural networks (CNNs) …
implementations of hardware accelerator for convolutional neural networks (CNNs) …
A collaborative framework for FPGA-based CNN design modeling and optimization
Convolutional neural network (CNN) has presented a great success in numerous areas and
has sparked an increasing interest in accelerating CNN using hardware like FPGAs …
has sparked an increasing interest in accelerating CNN using hardware like FPGAs …
Latency-driven design for FPGA-based convolutional neural networks
SI Venieris, CS Bouganis - 2017 27th International Conference …, 2017 - ieeexplore.ieee.org
In recent years, Convolutional Neural Networks (ConvNets) have become the quintessential
component of several state-of-the-art Artificial Intelligence tasks. Across the spectrum of …
component of several state-of-the-art Artificial Intelligence tasks. Across the spectrum of …
Fast and efficient implementation of convolutional neural networks on FPGA
State-of-the-art CNN models for Image recognition use deep networks with small filters
instead of shallow networks with large filters, because the former requires fewer weights. In …
instead of shallow networks with large filters, because the former requires fewer weights. In …
Throughput-optimized OpenCL-based FPGA accelerator for large-scale convolutional neural networks
Convolutional Neural Networks (CNNs) have gained popularity in many computer vision
applications such as image classification, face detection, and video analysis, because of …
applications such as image classification, face detection, and video analysis, because of …
Tinycnn: A tiny modular CNN accelerator for embedded FPGA
A Jahanshahi - arXiv preprint arXiv:1911.06777, 2019 - arxiv.org
In recent years, Convolutional Neural Network (CNN) based methods have achieved great
success in a large number of applications and have been among the most powerful and …
success in a large number of applications and have been among the most powerful and …
Memory optimization techniques for fpga based cnn implementations
M Shahshahani, P Goswami… - 2018 IEEE 13th Dallas …, 2018 - ieeexplore.ieee.org
Deep Learning has played an important role in the classification of images, speech
recognition, and natural language processing. Traditionally, these learning algorithms are …
recognition, and natural language processing. Traditionally, these learning algorithms are …