High-Performance In-Memory Bayesian Inference With Multi-Bit Ferroelectric FET

Conventional neural network-based machine learning algorithms often encounter difficulties in data-limited scenarios or where interpretability is critical. Conversely, Bayesian inference-based models excel with reliable uncertainty estimates and explainable predictions. Recently, many in-memory computing (IMC) architectures achieve exceptional computing capacity and efficiency for neural network tasks leveraging emerging non-volatile memory (NVM) technologies. However, their application in Bayesian inference remains limited because the operations in Bayesian inference differ substantially from those in neural networks. In this article, we introduce a compact in-memory Bayesian inference engine with high efficiency and performance utilizing a multi-bit ferroelectric field-effect transistor (FeFET). This design encodes a Bayesian model within a compact FeFET-based crossbar by mapping quantized probabilities to discrete FeFET states. Consequently, the crossbar’s outputs naturally represent the output posteriors of the Bayesian model. Our design facilitates efficient Bayesian inference, accommodating various input types and probability precisions, without additional calculation circuitry. As the first FeFET-based in-memory Bayesian inference engine, our design demonstrates a notable storage density of 26.32 Mb/mm² and a computing efficiency of 581.40 TOPS/W in a representative Bayesian classification task, indicating a 10.7×/43.4× compactness/efficiency improvement compared to the state-of-the-art alternative. Utilizing the proposed Bayesian inference engine, we develop a feature selection system that efficiently addresses a representative NP-hard optimization problem, showcasing our design’s capability and potential to enhance various Bayesian inference-based applications. Test results suggest that our design identifies the essential features, enhancing the model’s performance while reducing its complexity, surpassing the latest implementation in operation speed and algorithm efficiency by 2.9×/2.0×, respectively.