  We present the BrainScaleS-2 mobile system as a compact analog inference
engine based on the BrainScaleS-2 ASIC and demonstrate its capabilities at
classifying a medical electrocardiogram dataset. The analog network core of the
ASIC is utilized to perform the multiply-accumulate operations of a
convolutional deep neural network. We measure a total energy consumption of
192uJ for the ASIC and achieve a classification time of 276us per
electrocardiographic patient sample. Patients with atrial fibrillation are
correctly identified with a detection rate of 93.7(7)% at 14.0(10)% false
positives. The system is directly applicable to edge inference applications due
to its small size, power envelope and flexible I/O capabilities. Possible
future applications can furthermore combine conventional machine learning
layers with online-learning in spiking neural networks on a single
BrainScaleS-2 ASIC. The system has successfully participated and proven to
operate reliably in the independently judged competition
"Pilotinnovationswettbewerb 'Energieeffizientes KI-System'" of the German
Federal Ministry of Education and Research (BMBF).
