In this example, I set out to see if I could recreate the...

In this example, I set out to see if I could recreate the accuracy achieved for the BrainChip visual wake work detection using Edge Impulse. The original accuracy for the BrainChip VVW was 84.77% while Edge Impulse trained version was 83.2% which is very close to the original results and within what I would consider the margin of error.

Of course, visual wake word is just one application which benefits from the BrainChip architecture. I was therefore curious how the BrainChip architecture would perform against the different applications including keyword spotting, anomaly detection, and CIFAR classification. Using the same process as above I created Edge Impulse projects for the three remaining projects. Into these projects I loaded in the dataset BrainChip had used and created projects.

I have linked the projects at the end of the blog, however, you can see in the table below the alignment between what BrainChip achieved and what I was able to recreate with Edge Impulse relatively easily.

All told I am very impressed with the BrainChip Akida neuromorphic processor. The performance of the networks implemented is very good, while the power used is also exceptionally low. These two parameters are critical parameters for embedded solutions deployed at the edge.