The trend in this field is to do both training and classifying remotely on a desktop. It's hard to see the benefits of keeping everything on the same chip. Th

The trend in this field is to do both training and classifying remotely on a desktop. It's hard to see the benefits of keeping everything on the same chip. The primary reason would probably be latency in classification. One would have to argue that remote is slower than local computation. Remote involves sending the inputs/receiving the outputs and having the computation be done on a desktop, which even at its most basic, is going to be more powerful than an embedded. Local involves no communication, but has to use slower computational system. Since the typical input/output is on the order of a few MB, data transfer latency is likely to be low. Considering deep nets require significant number crunching for evaluation, the advantage by using a faster machine might make up for the data transfer losses. Thus while there might be benefits here, they aren't going to spectacular.