ProbLP：一个低精度概率推理框架
ProbLP: A framework for low-precision probabilistic inference
摘要：
贝叶斯推理是智能边缘设备中概率推理的一种强大机制。在这样的推断期间，低精度算术表示可以实现改进的能量效率。然而，其对推理准确性的影响尚未被理解。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，这是一个自动分析和设计低精度概率推理硬件的框架。它会根据最坏情况下的误差范围和硬件能量模型自动选择适当的节能表示。它为所产生的推理网络生成定制硬件，利用并行，流水线和低精度操作。该框架在几个嵌入式传感基准上进行了验证。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
