No. The architecture of the Loihi board is not the same as a traditional CPU, so I hesitate to use the word “CPU” to describe it. By “Loihi board”, I mean a collection of Loihi chips. In each chip, there are several neurocores, which contain circuitry to do the neuron activation function computation, as well as any connection weights (matrix operations) necessary. Each chip also contains a few lakemont CPUs, which are in charge of handling I/O and timing signals. If you want to know more about the architecture of the Loihi chip & Loihi board, I recommend posting on the INRC forums.
If you consider just execution time, it will be more “efficient” to perform the AveragePooling operation on a GPU, yes. However, if you are considering power, then no, it will probably still be more efficient to perform the AveragePooling on the Loihi chip. This is assuming a batch size of 1 though. For larger batch sizes, the GPU gets more efficient if you amortize the power across the size of the batch.
We discuss the relationship between energy use and batch sizes in this paper (although, you should note that this is for one specific application, so it may not necessary generalize to others).
If I recall correctly, the matrix multiplication operations are done on a per spike basis within the Loihi neurocores, but I’ll have to double check that.
Essentially, yes. The on-chip neurons is just a specially tuned nengo.Ensemble, but it functions the same as regular ensembles.