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.

中文翻译：

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ProbLP：低精度概率推断的框架

贝叶斯推理是智能边缘设备中概率推理的强大机制。在这样的推断期间，低精度算术表示可以提高能源效率。但是，它对推理精度的影响尚不明确。此外，通用硬件本身不支持低精度表示。为了解决这个问题，我们提出了ProbLP，这是一个自动化低精度概率推理硬件的分析和设计的框架。它会根据最坏情况的误差范围和硬件能效模型自动选择合适的能效表示形式。它利用并行性，流水线和低精度操作为生成的推理网络生成定制硬件。该框架已在多个嵌入式传感基准上得到验证。