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#author("2021-10-08T12:32:36+09:00","default:asl_member","asl_member")
#author("2021-10-08T12:32:42+09:00","default:asl_member","asl_member")
[[AIRBiS Project]]

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CENTER:COLOR(#990055){SIZE(40){''Parallelization and Hardware Mapping of CNN for AI-Enabled Biomedical System on FPGA'' }} 
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[[Yuki Okada]]

*AIRBiS progress meeting [#f5568590]

-[[AIRBiS Minutes]]

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*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|
|&#9745;&#65039;June 1 - June 31, 2020|Read papers|
|July 1 - July 31, 2020|Understanding and executing Mr. Nakamura's GT |
|August 1 - October 31|Implement own system architecture |
|September 1 - September 31|Create interim presentation |
|September 1 - November 31 |Simulate and Write paper to [[International ACM conference>https://etltc-acmchap-japanconf.org/]]|
|November 1 - January 31|Write 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]]