Let's see, first of all this is a research project not an attempt to build a commercial product (yet). Indeed it is not currently optimised to do analog matrix multiplication particularly well, but what prevents it from performing better is well understood and relatively easy to fix. Of course we are aware of commercial efforts, but this is a pretty long running research project, so not all of those necessarily existed, when it was set in motion. As an example wafer-scale integration was accomplished on BrainScaleS way before Cerebras pushed for it, clearly they have executed far better on building a commercial product around it.

Our motivation is to build large scale accelerated neuromorphic hardware and to prove that it can be useful. It is not particularly efficient or even feasible to train SNN with GPUs over large timescales, so eventually we will need to use on-chip learning. This paper could be seen as an intermediate step, it's useful to know that the hardware can be optimised in the very least as a base line for further experiments.

Applications are not our primary concern at the moment, for the most part we believe that once we have identified the right algorithm(s) and hardware that is able to support the implementation of these algorithms it will be possible to apply it to many problems. For ANNs backpropagation had been figured out a long time ago, but the recent successes started after 2010. To be clear for SNN inference our chip is far faster and has far better latency than a GPU and is roughly ~10x faster than Intel's Loihi. Application areas for that are admittedly niche, especially as long as we can't scale to far more neurons.