Bayesian Neural Networks (BNNs) can overcome the problem of overconfidence that plagues traditional frequentist deep neural networks, and are hence considered to be a key enabler for reliable AI systems. However, conventional hardware realizations of BNNs are resource intensive, requiring the implementation of random number generators for synaptic sampling. Owing to their inherent stochasticity during programming and read operations, nanoscale memristive devices can be directly leveraged for sampling, without the need for additional hardware resources. In this paper, we introduce a novel Phase Change Memory (PCM)-based hardware implementation for BNNs with binary synapses. The proposed architecture consists of separate weight and noise planes, in which PCM cells are configured and operated to represent the nominal values of weights and to generate the required noise for sampling, respectively. Using experimentally observed PCM noise characteristics, for the exemplary Breast Cancer Dataset classification problem, we obtain hardware accuracy and expected calibration error matching that of an 8-bit fixed-point (FxP8) implementation, with projected savings of over 9$\times$ in terms of core area transistor count.

翻译：贝叶斯神经网络（BNN）能够克服传统频率学派深度神经网络中普遍存在的过度自信问题，因此被视为实现可靠人工智能系统的关键。然而，BNN的传统硬件实现资源消耗巨大，需要为突触采样配备随机数生成器。纳米级忆阻器件在编程和读取过程中具有固有不稳定性，可直接用于采样，无需额外硬件资源。本文提出一种基于相变存储器（PCM）的新型BNN硬件实现方案，该方案采用二值突触。所提出的架构由独立的权重平面和噪声平面组成，其中PCM单元分别配置为表示权重的标称值和生成采样所需噪声。基于实验观测到的PCM噪声特性，以乳腺癌数据集分类问题为例，我们获得的硬件精度和期望校准误差与8位定点（FxP8）实现完全匹配，同时核心面积晶体管数量预计节省超过9倍。