  SpiNNaker is an ARM-based processor platform optimized for the simulation of
spiking neural networks. This brief describes the roadmap in going from the
current SPINNaker1 system, a 1 Million core machine in 130nm CMOS, to
SpiNNaker2, a 10 Million core machine in 22nm FDSOI. Apart from pure scaling,
we will take advantage of specific technology features, such as runtime
adaptive body biasing, to deliver cutting-edge power consumption. Power
management of the cores allows a wide range of workload adaptivity, i.e.
processor power scales with the complexity and activity of the spiking network.
Additional numerical accelerators will enhance the utility of SpiNNaker2 for
simulation of spiking neural networks as well as for executing conventional
deep neural networks. These measures should increase the simulation capacity of
the machine by a factor $>$50. The interplay between the two domains, i.e.
spiking and rate based, will provide an interesting field for algorithm
exploration on SpiNNaker2. Apart from the platforms' traditional usage as a
neuroscience exploration tool, the extended functionality opens up new
application areas such as automotive AI, tactile internet, industry 4.0 and
biomedical processing.
