  This paper presents the concepts behind the BrainScales (BSS) accelerated
analog neuromorphic computing architecture. It describes the second-generation
BrainScales-2 (BSS-2) version and its most recent in-silico realization, the
HICANN-X Application Specific Integrated Circuit (ASIC), as it has been
developed as part of the neuromorphic computing activities within the European
Human Brain Project (HBP). While the first generation is implemented in an
180nm process, the second generation uses 65nm technology. This allows the
integration of a digital plasticity processing unit, a highly-parallel micro
processor specially built for the computational needs of learning in an
accelerated analog neuromorphic systems. The presented architecture is based
upon a continuous-time, analog, physical model implementation of neurons and
synapses, resembling an analog neuromorphic accelerator attached to build-in
digital compute cores. While the analog part emulates the spike-based dynamics
of the neural network in continuous-time, the latter simulates biological
processes happening on a slower time-scale, like structural and parameter
changes. Compared to biological time-scales, the emulation is highly
accelerated, i.e. all time-constants are several orders of magnitude smaller
than in biology. Programmable ion channel emulation and inter-compartmental
conductances allow the modeling of nonlinear dendrites, back-propagating
action-potentials as well as NMDA and Calcium plateau potentials. To extend the
usability of the analog accelerator, it also supports vector-matrix
multiplication. Thereby, BSS-2 supports inference of deep convolutional
networks as well as local-learning with complex ensembles of spiking neurons
within the same substrate.
