The Big Benchmarking Roundup

Getting started with machine learning and edge computing

Over the last six months I’ve been looking at machine learning on the edge, publishing a series of articles trying to answer some of the questions that people have been asking about inferencing on embedded hardware.

But, after a half year of posts, talks, and videos, it’s all bit of a sprawling mess and the overall picture is of what’s really happening is rather confusing.

So here’s a great big benchmarking roundup!

Inferencing time in milli-seconds for the for MobileNet v1 SSD 0.75 depth model (left hand bars) and the MobileNet v2 SSD model (right hand bars), trained using the Common Objects in Context (COCO) dataset with an input size of 300×300. Stand alone platforms are shown in green, while the (single) bars for the Xnor AI2GO platform are timings for their proprietary binary weight model and are shown in blue. All other measurements using accelerator hardware attached to the Raspberry Pi 3, Model B+, are in yellow, while measurements on the Raspberry Pi 4, Model B, in red.

Although some people have dismissed the idea of benchmarks for inferencing as irrelevant because “…it’s training times that matter,” that doesn’t really seem justified. While if you take an academic approach to machine learning you often will train thousands of different models to find one that is ‘paper worthy’ but this does not seem to be how things work out in the world.

Instead for embedded systems training is a sunk cost with the final model being used thousands, perhaps even millions, of times depending on how many…