Yuki Okada
をテンプレートにして作成
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開始行:
[[AIRBiS Project]]
----
CENTER:COLOR(#990055){SIZE(40){''Hardware Acceleration of Convolution Neural Network on FPGA for AIRBiS''}}
----
*AIRBiS progress meeting [#f5568590]
-[[AIRBiS Minutes]]
----
*Background [#hdf7ab88]
With the Coronavirus disease (COVID-19)
disrupting large part of the world, the number
of patients has grown progressively. For
dealing with this serious emergency, accurate
diagnosis and fast reporting are two
significant mechanisms.
*Research Goal [#peb277c4]
My research goal is to implement,
optimize, and evaluate a deep neural
network (i.e., CNN) for an ongoing project
named AIRBiS System using FPGA and
based on packet-switched 2D network on
chip.
1.Given the resulted source code (TensorFlow,
Python) from software implementation, to realize a
CNN for pneumonia detection system on hardware
(Xilinx platform, conventional FPGA board)
2.Perform evaluation on non-NoC FPGA w.r.t. some
key indices:
Accuracy
Power consumption
Complexity
Etc..
*Research Schedule [#q0b5e376]
|Date|Task|
|October - December, 2021|A research on speedup with a single FPGA|
|November - February | Write a paper for submission to ETLTC |
|March - July |Connecting multiple FPGAs |
|August - December |Speed improvement experiment when using multiple FPGAs |
|November - February |Write a MS thesis|
CENTER:COLOR(red){''Schedule last Updated on: MM/DD/YYYY ???''}
*Researech Progress [#m562b08f]
slide:
&ref(June progress report.pdf);
&ref(July progress report.pdf);
&ref(August progress report.pdf);
Report:
&ref(AugustProgressReports1250129YuukiOkada.pptx);
*References [#f5a18ef1]
-[[My SharedGoogle Folder>https://drive.google.com/drive/folders/1dlzzlEMU1MNHtDTNkS6rPjWpJllpsCWl?usp=sharing]]
-[[Power Estimation with Vivado>https://www.xilinx.com/video/hardware/power-estimation-analysis-using-vivado.html]]
終了行:
[[AIRBiS Project]]
----
CENTER:COLOR(#990055){SIZE(40){''Hardware Acceleration of Convolution Neural Network on FPGA for AIRBiS''}}
----
*AIRBiS progress meeting [#f5568590]
-[[AIRBiS Minutes]]
----
*Background [#hdf7ab88]
With the Coronavirus disease (COVID-19)
disrupting large part of the world, the number
of patients has grown progressively. For
dealing with this serious emergency, accurate
diagnosis and fast reporting are two
significant mechanisms.
*Research Goal [#peb277c4]
My research goal is to implement,
optimize, and evaluate a deep neural
network (i.e., CNN) for an ongoing project
named AIRBiS System using FPGA and
based on packet-switched 2D network on
chip.
1.Given the resulted source code (TensorFlow,
Python) from software implementation, to realize a
CNN for pneumonia detection system on hardware
(Xilinx platform, conventional FPGA board)
2.Perform evaluation on non-NoC FPGA w.r.t. some
key indices:
Accuracy
Power consumption
Complexity
Etc..
*Research Schedule [#q0b5e376]
|Date|Task|
|October - December, 2021|A research on speedup with a single FPGA|
|November - February | Write a paper for submission to ETLTC |
|March - July |Connecting multiple FPGAs |
|August - December |Speed improvement experiment when using multiple FPGAs |
|November - February |Write a MS thesis|
CENTER:COLOR(red){''Schedule last Updated on: MM/DD/YYYY ???''}
*Researech Progress [#m562b08f]
slide:
&ref(June progress report.pdf);
&ref(July progress report.pdf);
&ref(August progress report.pdf);
Report:
&ref(AugustProgressReports1250129YuukiOkada.pptx);
*References [#f5a18ef1]
-[[My SharedGoogle Folder>https://drive.google.com/drive/folders/1dlzzlEMU1MNHtDTNkS6rPjWpJllpsCWl?usp=sharing]]
-[[Power Estimation with Vivado>https://www.xilinx.com/video/hardware/power-estimation-analysis-using-vivado.html]]
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