PIU: A 248GOPS/W Stream-Based Processor for Irregular Probabilistic Inference Networks Using Precision-Scalable Posit Arithmetic in 28nm

📋 论文概要

提出一种名为PIU的流式处理器，专门用于不规则概率推理网络，通过使用精度可缩放的Posit算术实现高能效计算，解决了神经网络可解释性差、依赖大量训练数据的问题。该处理器在流式处理中实现了248GOPS/W的能效。

💡 主要创新点

• 首次将精度可缩放的Posit算术应用于概率推理网络的流式处理，支持不规则网络结构的高效计算
• 设计了面向不规则概率推理网络的高能效流式架构，达到248GOPS/W的能效比
• 通过硬件-软件协同优化，实现了对动态精度需求的自适应调整，提升处理效率

🏷 关键词

📄 原文摘要

devices, their usage is also criticized due to lack of explainability, inability to include domain knowledge, and a need for large volumes of training data. To overcome this, researchers are increasingly using probabilistic models as a part of the system [1][2][3] (Fig. 9.4.1). For example, Stelzner et al. [2] complements neural networks with probabilistic models for efficient unsupervised scene understanding robust to noise. Zheng et al. [3] uses a probabilistic model for end-to-end semantic environment mapping during robotic navigation. While sampling techniques are usually used for approximate inference with probabilistic models, fast exact inference is often tractable by using Sum-Product Networks (SPN, also called probabilistic circuits) [4]. This SPN-based inference is preferred over sampling techniques because it provides deterministic results, avoids

👥 作者与机构

Nimish Shah, Laura Isabel Galindez Olascoaga, Shirui Zhao, Wannes Meert, Marian Verhelst

KU Leuven - MICAS, Leuven, Belgium While deep neural networks have become an indispensable tool in today’s smart