The main focus of this week was revolving around our design presentation. My main work for this week was spent revolving around preparing the slideshow at the beginning of the week, helping Kavish get ready to present, and then beginning the transition of information from the slideshow onto the final design report.
I also spent more time this week looking into the RNN and beginning development. I am looking into what libraries would work best for a real-time inference model. I decided to begin development using Tensorflow simply because it is the most predominant library used in the industry, considering it has the largest amount of optimization and thus would run the fastest, thus lowering our latency as much as possible. As of right now, I am just developing the model, so my main goal over the next week is to spend more time focusing on finishing that so we can get to testing as much as possible. We also need to develop a training set for the RNN, so I am currently setting up the code to take our videos and transform them into HPE vector outputs so that we can use those to train our RNN. I need to figure out where I should do this process, as it might take a good amount of computing power, so I need to determine whether we can do this on the ECE machines or if it might be worth it to invest in AWS services to run these.
I believe I am slightly behind schedule, as we expect the RNN model to be finished soon. It might be worth it to push the RNN scheduling a bit further back, as the RNN model might take more time than we anticipated, partially due to workload from other classes. As such, it might be worth it to merge the time in which we are testing the RNN and developing it, as that is something that can be combined, since part of the RNN development time will include developing the LSTM model for comparison. As such, we can test the GRU model in the mean time.
For next week, I ideally want to finish the base GRU RNN model, as that would keep us as on track as possible, and in a good spot heading into spring break, in which I can work on testing and model comparison.