  Challenging the Nvidia monopoly, dedicated AI-accelerator chips have begun
emerging for tackling the computational challenge that the inference and,
especially, the training of modern deep neural networks (DNNs) poses to modern
computers. The field has been ridden with studies assessing the performance of
these contestants across various DNN model types. However, AI-experts are aware
of the limitations of current DNNs and have been working towards the fourth AI
wave which will, arguably, rely on more biologically inspired models,
predominantly on spiking neural networks (SNNs). At the same time, GPUs have
been heavily used for simulating such models in the field of computational
neuroscience, yet AI-chips have not been tested on such workloads. The current
paper aims at filling this important gap by evaluating multiple, cutting-edge
AI-chips (Graphcore IPU, GroqChip, Nvidia GPU with Tensor Cores and Google TPU)
on simulating a highly biologically detailed model of a brain region, the
inferior olive (IO). This IO application stress-tests the different
AI-platforms for highlighting architectural tradeoffs by varying its compute
density, memory requirements and floating-point numerical accuracy. Our
performance analysis reveals that the simulation problem maps extremely well
onto the GPU and TPU architectures, which for networks of 125,000 cells leads
to a 28x respectively 1,208x speedup over CPU runtimes. At this speed, the TPU
sets a new record for largest real-time IO simulation. The GroqChip outperforms
both platforms for small networks but, due to implementing some floating-point
operations at reduced accuracy, is found not yet usable for brain simulation.
