Acceleration of probabilistic reasoning through custom processor architecture

TL;DR

This paper introduces a custom processor architecture designed to accelerate probabilistic reasoning, specifically sum-product networks, achieving significant throughput improvements over existing GPU platforms.

Contribution

It presents a novel programmable processor architecture optimized for sum-product networks, enhancing efficiency and throughput in probabilistic reasoning tasks.

Findings

12x throughput improvement over Nvidia Jetson TX2

Fewer computational and memory units needed

Optimized datapath and memory hierarchy for sum-product networks

Abstract

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.