GitHub - Ushma30/CNN-MobileNet-V1-implementation-on-AWS-FPGA-using-OpenCL: Increasing the accuracy of Convolutional Neural Networks (CNNs) has become a recent research focus in computer vision applications. Smaller CNN architectures like SqueezeNet and MobileNet can demonstrate accelerated performance on FPGAs and GPUs due to smaller model size and fewer network parameters. Implementation of CNNs on accelerators have two important benefits - GPUs provide thread-level parallelism to achieve higher throughput and FPGAs offer a customizable application-specific datapath. These two reasons make these platforms better suited for convolution like operations which involve huge data. This project aims to implement one such CNN architecture, MobileNet on an Image dataset in OpenCL, thereby comparing kernel execution time and memory bandwidth usage on FPGA and GPU

Ushma30 / CNN-MobileNet-V1-implementation-on-AWS-FPGA-using-OpenCL Public

forked from anerisheth19/CNN-MobileNet-V1-implementation-on-AWS-FPGA-using-OpenCL

Notifications You must be signed in to change notification settings
Fork 0
Star 0

Increasing the accuracy of Convolutional Neural Networks (CNNs) has become a recent research focus in computer vision applications. Smaller CNN architectures like SqueezeNet and MobileNet can demonstrate accelerated performance on FPGAs and GPUs due to smaller model size and fewer network parameters. Implementation of CNNs on accelerators have t…

0 stars 3 forks Branches Tags Activity

Star

Notifications

Branches Tags

Name		Name	Last commit message	Last commit date
Latest commit History 11 Commits
Log_Rooflines_Updated.PNG		Log_Rooflines_Updated.PNG
MobileNet.c		MobileNet.c
MobileNet_13Layers.c		MobileNet_13Layers.c
MobileNet_L5.c		MobileNet_L5.c
Project_Proposal.pdf		Project_Proposal.pdf
Roofline_Calculations.pdf		Roofline_Calculations.pdf
keras.py		keras.py
kernel.cl		kernel.cl
makefile		makefile
mobilenet_1_0_224_tf.h5		mobilenet_1_0_224_tf.h5
script.pbs		script.pbs