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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.
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..
| Date | Task |
| ☑️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 |
| November 1 - January 31 | Write thesis |
slide: &ref(): File not found: "June progress report.pdf" at page "M OKADA Yuki"; &ref(): File not found: "July progress report.pdf" at page "M OKADA Yuki"; &ref(): File not found: "August progress report.pdf" at page "M OKADA Yuki"; Report: &ref(): File not found: "AugustProgressReports1250129YuukiOkada.pptx" at page "M OKADA Yuki";
