Team’s Status Report for 2/10/24

As we analyze the project right now, the most significant risks that could jeopardize the success of the project are not being able to port our HPE model onto FPGA, not having enough data to train a proper RNN, getting inconsistent response when working in Real-Time, and having communication failures. If there are any major problems with our FPGA, our contingency is to reduce our MVP and port all parts of our pipeline directly onto a Jetson. Although we seem to have enough data right now, we have found a couple of datasets which we could try to combine with some additional pre-processing if they are needed for training. Finally, if there are any failures due to latency requirement, we have prepared enough slack to increase latency if necessary. Compared to our original design, we are now considering an augmented version of the pipeline. We plan to run Human Pose Estimation and use the outputs from that model to train our ASL classification RNN. We are still exploring this concept but we hope that will reduce our model size. We are also considering moving the RNN from the computer and running on the Jetson. This will allow use to develop a more compact final product.  Of course, this adds another part to our parts list; however, we will discuss this idea more with faculty before implementing this solution. We are still on schedule and no updates are necessary as of right now.