  Probabilistic reasoning is an essential tool for robust decision-making
systems because of its ability to explicitly handle real-world uncertainty,
constraints and causal relations. Consequently, researchers are developing
hybrid models by combining Deep Learning with probabilistic reasoning for
safety-critical applications like self-driving vehicles, autonomous drones,
etc. However, probabilistic reasoning kernels do not execute efficiently on
CPUs or GPUs. This paper, therefore, proposes a custom programmable processor
to accelerate sum-product networks, an important probabilistic reasoning
execution kernel. The processor has an optimized datapath architecture and
memory hierarchy optimized for sum-product networks execution. Experimental
results show that the processor, while requiring fewer computational and memory
units, achieves a 12x throughput benefit over the Nvidia Jetson TX2 embedded
GPU platform.
