  We implemented two neural network based benchmark tasks on a prototype chip
of the second-generation SpiNNaker (SpiNNaker 2) neuromorphic system: keyword
spotting and adaptive robotic control. Keyword spotting is commonly used in
smart speakers to listen for wake words, and adaptive control is used in
robotic applications to adapt to unknown dynamics in an online fashion. We
highlight the benefit of a multiply accumulate (MAC) array in the SpiNNaker 2
prototype which is ordinarily used in rate-based machine learning networks when
employed in a neuromorphic, spiking context. In addition, the same benchmark
tasks have been implemented on the Loihi neuromorphic chip, giving a
side-by-side comparison regarding power consumption and computation time. While
Loihi shows better efficiency when less complicated vector-matrix
multiplication is involved, with the MAC array, the SpiNNaker 2 prototype shows
better efficiency when high dimensional vector-matrix multiplication is
involved.
