ProbLP: A framework for low-precision probabilistic inference

Marian Verhelst, Laura I. Galindez Olascoaga, Nimish Shah, Wannes Meert

Publication date: 27 February 2021

Abstract: Bayesian reasoning is a powerful mechanism for probabilistic inference in smart edge-devices. During such inferences, a low-precision arithmetic representation can enable improved energy efficiency. However, its impact on inference accuracy is not yet understood. Furthermore, general-purpose hardware does not natively support low-precision representation. To address this, we propose ProbLP, a framework that automates the analysis and design of low-precision probabilistic inference hardware. It automatically chooses an appropriate energy-efficient representation based on worst-case error-bounds and hardware energy-models. It generates custom hardware for the resulting inference network exploiting parallelism, pipelining and low-precision operation. The framework is validated on several embedded-sensing benchmarks.

Has companion code repository: https://github.com/nimish15shah/ProbLP

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