Jun 02 2020
“I’m Givin’ Her All She’s Got, Captain”
In my last post we talked about an experiment with neural networks we ran here at NGD Systems. The goal of this experiment was to test energy efficiency and accuracy of 5 different image detection and object recognition algorithms. These are complex neural networks often run in edge applications. We wanted to test them in three different hardware configurations to see which configuration gave us the lowest power utilization (important for edge devices) while still delivering the highest accuracy possible.
The hardware sets we chose to test were:
The 5 algorithms we chose to run were:
You can find the surprising summary of results from our research in Figure 2 below.
Figure: 2 – Object Recognition and Detection at the Edge CPU vs. GPU vs. Computational Storage
What you see in these results is the amount of energy consumed in processing each video frame using the 5 different Object Detection Algorithms in each of the different compute environments. We also wanted to measure accuracy in terms of IoU (Intersection over Union) for each of the runs.
The bottom line is that in almost all cases, using an NVMe Computational Storage SSD to run the Object Detection and tracking algorithms is significantly more energy efficient than either a CPU or a GPU based approach. This efficiency comes WITHOUT sacrificing accuracy or increasing system cost.
So, if you “need more power” for your edge application, the most energy efficient way to increase your system compute capabilities is through the deployment of Computational Storage enabled SSD’s. More storage, more compute, less power, and no additional cost. Something worth looking at.