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.

翻译：Bayesian 推理是智能边缘装置概率推理的有力机制。在这种推理中,低精度算术代表可以提高能源效率,但是,它对推理准确性的影响还没有得到理解。此外,一般用途硬件本身并不支持低精度代表制。为了解决这个问题,我们提议ProbLP,这是一个使低精度概率推理硬件的分析和设计自动化的框架。它根据最差的错误和硬件能源模型自动选择适当的节能代表制。它为导致利用平行、管道和低精度操作的推论网络生成定制硬件。这个框架根据若干嵌入式测量基准进行验证。