Synaptic stochasticity is an important feature of biological neural networks that is not widely explored in analog memristor networks. Synaptic Sampling Machine (SSM) is one of the recent models of the neural network that explores the importance of the synaptic stochasticity. In this paper, we present a memristive Echo State Network (ESN) with Extended-SSM (ESSM). The circuit-level design of the single synaptic sampling cell that can introduce stochasticity to the neural network is presented. The architecture of synaptic sampling cells is proposed that have the ability to adaptively reprogram the arrays and respond to stimuli of various strengths. The effect of stochasticity is achieved by randomly blocking the input with the probability that follows Bernoulli distribution, and can lead to the reduction of the memory capacity requirements. The blocking signals are randomly generated using Circular Shift Registers (CSRs). The network processing is handled in analog domain and the training is performed offline. The performance of the neural network is analyzed with a view to benchmark for hardware performance without compromising the system performance. The neural system was tested on ECG, MNIST, Fashion MNIST and CIFAR10 dataset for classification problem. The advantage of memristive CSR in comparison with conventional CMOS based CSR is presented. The ESSM-ESN performance is evaluated with the effect of device variations like resistance variations, noise and quantization. The advantage of ESSM-ESN is demonstrated in terms of performance and power requirements in comparison with other neural architectures.

中文翻译：

---

ESSM：具有随机回声状态神经忆阻电路的扩展突触采样机

突触随机性是生物神经网络的一个重要特征，在模拟忆阻器网络中尚未得到广泛研究。突触采样机（SSM）是神经网络的最新模型之一，它探索突触随机性的重要性。在本文中，我们提出了一种具有扩展 SSM（ESSM）的忆阻回声状态网络（ESN）。提出了可以向神经网络引入随机性的单突触采样单元的电路级设计。提出了突触采样单元的架构，该架构能够自适应地重新编程阵列并响应各种强度的刺激。随机性的效果是通过按照伯努利分布的概率随机阻塞输入来实现的，并且可以导致内存容量需求的减少。阻塞信号是使用循环移位寄存器（CSR）随机生成的。网络处理在模拟域中进行，训练是离线进行的。分析神经网络的性能，以在不影响系统性能的情况下对硬件性能进行基准测试。该神经系统在 ECG、MNIST、Fashion MNIST 和 CIFAR10 数据集上进行了分类问题测试。介绍了忆阻式 CSR 与传统基于 CMOS 的 CSR 相比的优点。 ESSM-ESN 性能通过电阻变化、噪声和量化等器件变化的影响进行评估。与其他神经架构相比，ESSM-ESN 的优势体现在性能和功耗要求方面。