  The neuromorphic BrainScaleS-2 ASIC comprises mixed-signal neurons and
synapse circuits as well as two versatile digital microprocessors. Primarily
designed to emulate spiking neural networks, the system can also operate in a
vector-matrix multiplication and accumulation mode for artificial neural
networks. Analog multiplication is carried out in the synapse circuits, while
the results are accumulated on the neurons' membrane capacitors. Designed as an
analog, in-memory computing device, it promises high energy efficiency.
Fixed-pattern noise and trial-to-trial variations, however, require the
implemented networks to cope with a certain level of perturbations. Further
limitations are imposed by the digital resolution of the input values (5 bit),
matrix weights (6 bit) and resulting neuron activations (8 bit). In this paper,
we discuss BrainScaleS-2 as an analog inference accelerator and present
calibration as well as optimization strategies, highlighting the advantages of
training with hardware in the loop. Among other benchmarks, we classify the
MNIST handwritten digits dataset using a two-dimensional convolution and two
dense layers. We reach 98.0% test accuracy, closely matching the performance of
the same network evaluated in software.
