  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.
