To conclude, it is clear that convolutional networks (as a field) are continuing to evolve, as seen in the novel architectures of several networks we have implemented in this article. Indeed, the strengths of convolutions that were historically leveraged for image classification are even more pivotal as we look towards more difficult computer vision tasks.

Using the approach we outlined at the start of this article, our hardware is therefore primed to accelerate the next generation of models — in turn enabling more ambitious, novel applications at edge to enterprise scale, in everything from drones to datacentres. If you are interested in bench-marking and modelling your enterprise’s computer vision workflows on our optical systems — contact us at to find out more about our beta programme.