Deep neural network approximation for custom hardware: Where we've been, where we're going
Deep neural networks have proven to be particularly effective in visual and audio
recognition tasks. Existing models tend to be computationally expensive and memory …
recognition tasks. Existing models tend to be computationally expensive and memory …
Efficient edge-AI application deployment for FPGAs
Field Programmable Gate Array (FPGA) accelerators have been widely adopted for artificial
intelligence (AI) applications on edge devices (Edge-AI) utilizing Deep Neural Networks …
intelligence (AI) applications on edge devices (Edge-AI) utilizing Deep Neural Networks …
Deepstrike: Remotely-guided fault injection attacks on dnn accelerator in cloud-fpga
As Field-programmable gate arrays (FPGAs) are widely adopted in clouds to accelerate
Deep Neural Networks (DNN), such virtualization environments have posed many new …
Deep Neural Networks (DNN), such virtualization environments have posed many new …
A high-throughput oversampled polyphase filter bank using Vivado HLS and PYNQ on a RFSoC
Many digital signal processing applications require a channelizer capable of moving
sections of the incoming spectrum to baseband quickly and efficiently with minimal spectral …
sections of the incoming spectrum to baseband quickly and efficiently with minimal spectral …
Control and visualisation of a software defined radio system on the Xilinx RFSoC platform using the PYNQ framework
J Goldsmith, C Ramsay, D Northcote, KW Barlee… - IEEE …, 2020 - ieeexplore.ieee.org
The availability of commercial Radio Frequency System on Chip (RFSoC) devices brings
new possibilities for implementing Software Defined Radio (SDR) systems. Such systems …
new possibilities for implementing Software Defined Radio (SDR) systems. Such systems …
Exploration of activation fault reliability in quantized systolic array-based dnn accelerators
The stringent requirements for the Deep Neural Networks (DNNs) accelerator's reliability
stand along with the need for reducing the computational burden on the hardware platforms …
stand along with the need for reducing the computational burden on the hardware platforms …
Implementation of CNN on Zynq based FPGA for Real-time Object Detection
The aim of this work is to implement a Convolutional Neural Network (CNN) using a Python
framework on Xilinx® Zynq® based Field Programmable Gate Array (FPGA). And the …
framework on Xilinx® Zynq® based Field Programmable Gate Array (FPGA). And the …
Prototype of low complexity CNN hardware accelerator with FPGA-based PYNQ platform for dual-mode biometrics recognition
In this study, the effective low-complexity convolutional neural network (CNN) inference
network is implemented by the FPGA-based hardware accelerator for dual-mode biometric …
network is implemented by the FPGA-based hardware accelerator for dual-mode biometric …
Novel casestudy and benchmarking of AlexNet for edge AI: From CPU and GPU to FPGA
F Al-Ali, TD Gamage… - 2020 IEEE Canadian …, 2020 - ieeexplore.ieee.org
Convolutional Neural Networks (CNNs) require massive parallelism due to the high-
precision floating-point arithmetic operations they perform. So, demand of processing power …
precision floating-point arithmetic operations they perform. So, demand of processing power …
A skeleton-based action recognition system for medical condition detection
J Yin, J Han, C Wang, B Zhang… - 2019 IEEE Biomedical …, 2019 - ieeexplore.ieee.org
Progress in skeleton-based action recognition has enabled the contactless, ceaseless and
portable surveillance on human daily behaviors which helps to reveal health hazards …
portable surveillance on human daily behaviors which helps to reveal health hazards …