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

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

Abstract: While deep neural networks have become an indispensable tool in today's smart 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 error-accumulation, and can enable inference in discrete probabilistic programs.