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Joshua’s Status Report for 12/4

This week, I made several adjustments to priorities before the end of semester. Firstly, James was making good progress on the implementation of FSRCNN-s, which meant that getting weights for the model was integral for our final demo and deliverable. Taking into account the potential worst-case scenario, I did research on publicly available weights for our CNN model, and did comparison between the dataset they used to see if they would be suitable, in the possible event that the training for our new model could not be done in time. After talking with the team, I reevaluated our priorities realized that finishing and printing our CAD model would have to be put on the backburner –  figuring out hyperparameters and looking through possible solutions with James was much more impertinent, in order to meet our deliverables by the end of the semester.

In terms of the final demo, I voiced several ideas on the structure of how we could present, including a multi-monitor setup, as well as interaction with the listeners, such as allowing them to rate which video they thought was the better one, as well as prompting them to give a subjective score, etc.

I made the final presentation and also prepared to deliver it. In doing so, I also worked on the final report, detailing a lot on our testing and metrics on our initial model, even though we’ve moved on from that one, as to fully document our developmental process.

In terms of the final video, I discussed with my team on what approaches we could take, and since none of us really had any video-editing experience, we decided to prepare in advance. I looked at videos from previous Capstone courses, specifically the winners of last semester, as I thought their video was correctly paced and well-edited.

(This report was made late, and was added on December 12th.)

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Kunal’s Status Report (12/4)

We finished up benchmarking the latencies of our fsrcnn model and I wrote a profiler that would take upscaled frames and build metrics based on the quality of these images. This we will use in the final demo to compare and contrast latencies of the software model running on the hardware device. The nature of the computation involves a scan over the pixel set and a buffering mechanism for the previous pixels fed into this pipeline.  We are actively tuning the model and analyzing our results, we expect to get to a significantly lower latency and be able to profile the results much more efficiently.

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Team Status Report for 12/04

James continued to work on squeezing performance from the fsrcnn model, but ran into diminishing returns. Using fixed weights allowed for some additional improvements in memory accessing, and since we have fixed weights, we have the ability to do this. Integration with the host side led to additional slowdowns. Thinking of ways to improve this, a multikernel approach was decided and James began writing this. He expects to finish implementing this by the end of the week of 11/29.

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James’s Status for 12/04

From last week have an implementation of fsrcnn which runs faster than srcnn, still slow though. One optimisation that I tested was using fixed weights as opposed to weights stored in host-side memory which is mapped to the kernel. This led to a decent improvement in latency but not enough to meet our initial specifications. Porting and integrating with host code has produced further slowdowns. Trying to remedy this with a multikerneled approach which should be finished by tonight. Will be focusing on writing the paper, the video, and making a narrative to sell what we have for the coming week, as we aren’t in the position schedule wise to try for more optimisations, even if that’s what I would like to do.

Project-management-wise, I also helped Josh practice for the Wednesday presentation on Tuesday.