具有随机突触的神经采样机允许类脑学习和推理
Neural Sampling Machine with Stochastic Synapse allows Brain-like Learning and Inference
摘要：
许多现实世界的关键任务应用程序需要从噪声数据中持续在线学习，并在定义的置信水平下进行实时决策。概率模型和随机神经网络可以显式地处理数据中的不确定性，并允许动态自适应学习，但它们在低功耗基板上的实现仍然是一个挑战。在这里，我们介绍了一种新的硬件结构，实现了一类新的随机NN称为神经采样机，利用突触连接的随机性近似贝叶斯推断。利用新兴材料和器件中原子水平上的固有非线性和随机性，使我们能够捕获生物突触中分子水平上的突触随机性。我们实验证明在硅片上的混合随机突触配对的铁电场效应晶体管为基础的模拟重量细胞与两端随机选择器元件。这样的随机突触可以集成在用于存储器中计算的完善的交叉杆阵列架构内。我们的实验表明，绝缘体和金属状态之间的选择器元件的固有的随机切换引入了一个乘性的随机噪声NSM的突触内的样品的FeFET的电导状态，无论是在学习和推理。我们执行网络级模拟，以突出由NSM的随机突触引入的显著的自动权重归一化功能，该功能为持续在线学习铺平了道路，而无需任何离线批量归一化。我们还展示了贝叶斯推理能力的随机突触在推理模式，从而占数据的不确定性。我们在标准图像分类任务上报告了98.25%的准确率，以及旋转样本中数据不确定性的估计。
英文摘要：
Many real-world mission-critical applications require continual online learning from noisy data and real-time decision making with a defined confidence level. Probabilistic models and stochastic neural networks can explicitly handle uncertainty in data and allow adaptive learning-on-the-fly, but their implementation in a low-power substrate remains a challenge. Here, we introduce a novel hardware fabric that implements a new class of stochastic NN called Neural-Sampling-Machine that exploits stochasticity in synaptic connections for approximate Bayesian inference. Harnessing the inherent non-linearities and stochasticity occurring at the atomic level in emerging materials and devices allows us to capture the synaptic stochasticity occurring at the molecular level in biological synapses. We experimentally demonstrate in-silico hybrid stochastic synapse by pairing a ferroelectric field-effect transistor -based analog weight cell with a two-terminal stochastic selector element. Such a stochastic synapse can be integrated within the well-established crossbar array architecture for compute-in-memory. We experimentally show that the inherent stochastic switching of the selector element between the insulator and metallic state introduces a multiplicative stochastic noise within the synapses of NSM that samples the conductance states of the FeFET, both during learning and inference. We perform network-level simulations to highlight the salient automatic weight normalization feature introduced by the stochastic synapses of the NSM that paves the way for continual online learning without any offline Batch Normalization. We also showcase the Bayesian inferencing capability introduced by the stochastic synapse during inference mode, thus accounting for uncertainty in data. We report 98.25%accuracy on standard image classification task as well as estimation of data uncertainty in rotated samples.
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