Due to the continuous growth of hardware neuromorphic systems, the need for high-speed, low-power, and energy-efficient computer architectures is increasing. Memristors-based neural networks are a promising solution for low-power neuromorphic systems. Spiking neural networks (SNNs) have been considered the optimal hardware implementation of these systems. Previous studies of SNNs rely on complex circuit to implement in situ bio-plausible STDP learning using memristors, which is computationally challenging. In this paper, we propose an SNN that performs both in situ learning and inference using a new efficient programming technique. Our interest lies in applying the winner-takes-all (WTA) mechanism in the SNN architecture used with recurrently connected neurons, allowing real-time processing of patterns. We provide a programming circuit that enables better weight modulation with less power consumption and less space occupation, using a generalized enhanced memristor model (EGM). The proposed programming circuit is connected to leaky integrate-and-fire (LIF) neurons included in a crossbar architecture to perform recognition task. The simulation results not only prove the correctness of the design, but also offer an efficient implementation in terms of area, energy, accuracy, as well as the ability to classify 40,000 images per second.

由于硬件神经形态系统的不断增长，对高速、低功耗和高能效的计算机架构的需求正在增加。基于忆阻器的神经网络是低功耗神经形态系统的有前途的解决方案。脉冲神经网络 (SNN) 已被认为是这些系统的最佳硬件实现。先前对 SNN 的研究依赖于复杂的电路来使用忆阻器实现原位生物可信 STDP 学习，这在计算上具有挑战性。在本文中，我们提出了一种使用新的高效编程技术执行原位学习和推理的 SNN。我们的兴趣在于在用于循环连接神经元的 SNN 架构中应用赢家通吃 (WTA) 机制，从而实现模式的实时处理。我们提供了一种编程电路，该电路使用通用增强型忆阻器模型 (EGM)，能够以更少的功耗和更少的空间占用实现更好的权重调制。所提出的编程电路连接到交叉开关架构中包含的泄漏积分和发射 (LIF) 神经元，以执行识别任务。仿真结果不仅证明了设计的正确性，而且在面积、能量、精度以及每秒分类 40,000 张图像的能力方面提供了有效的实现。