Accomplishments
Simon and I researched FPGA hardware acceleration and discussed our results briefly in order to understand how it works to a better degree and we also deliberated on which FPGA would be good to use for our project. While we saw multiple articles online where Xilinx FPGAs are being used for accelerating ML algorithms, we came to the conclusion that the exact type of FPGA doesn’t really matter and we decided that we could use an Intel Altera board (currently planning on using the DE10-Standard, can order the DE10-Lite if the DE10-Standard is taken by another team). We also found that it would be better to use a Cyclone V FPGA rather than a Cyclone IV FPGA like the DE0-Nano, because Cyclone V boards support OpenCL, which is a framework we are heavily considering for hardware acceleration.
Furthermore, I came across this article that seems to be very relevant for our project (https://www.researchgate.net/publication/338481306_Hardware_Acceleration_of_Computer_Vision_and_Deep_Learning_Algorithms_on_the_Edge_using_OpenCL). This article seems to implement hardware acceleration for computer vision algorithms using OpenCL, a high level synthesis framework that converts kernel code to RTL. An even more relevant detail in this article is that this was all done using a Cyclone V Altera board, which we are planning to use by requesting from the project inventory (DE10-Standard). I also edited the Gantt Chart to better reflect our workflow during Spring Break.
Project Schedule and Progress
Fortunately, my progress is on schedule, and during Monday’s mandatory lab meeting, Simon and I will discuss our findings in more detail and start working on the design of our RTL/kernel code implementation, if time allows. This portion of the project is extremely important because our approach for hardware acceleration is vital for our final product.
Goals for the Next Week
In the next week, Simon and I hope to finish designing our RTL/kernel implementation and getting an FPGA. I will also hope to complete a significant portion of the cashier time detection algorithm implementation in that time, and we hope to get cameras delivered so we can test our CV algorithms in the future.