| Autor: |
Shah, Nimish, Olascoaga, Laura I. Galindez, Meert, Wannes, Verhelst, Marian |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
Design, Automation & Test in Europe Conference & Exhibition (DATE) 2020 |
| Druh dokumentu: |
Working Paper |
| DOI: |
10.23919/DATE48585.2020.9116326 |
| Popis: |
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. |
| Databáze: |
arXiv |
| Externí odkaz: |
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