Memristor-based platforms could be used to create compact and energy-efficient artificial intelligence (AI) edge-computing systems due to their parallel computation ability in the analogue domain. However, systems based on memristor arrays face challenges implementing real-time AI algorithms with fully on-device learning due to reliability issues, such as low yield, poor uniformity and endurance problems. Here we report an analogue computing platform based on a selector-less analogue memristor array. We use interfacial-type titanium oxide memristors with a gradual oxygen distribution that exhibit high reliability, high linearity, forming-free attribute and self-rectification. Our platform—which consists of a selector-less (one-memristor) 1 K (32 × 32) crossbar array, peripheral circuitry and digital controller—can run AI algorithms in the analogue domain by self-calibration without compensation operations or pretraining. We illustrate the capabilities of the system with real-time video foreground and background separation, achieving an average peak signal-to-noise ratio of 30.49 dB and a structural similarity index measure of 0.81; these values are similar to those of simulations for the ideal case.

由于基于忆阻器的平台在模拟领域的并行计算能力，因此可用于创建紧凑且节能的人工智能 （AI） 边缘计算系统。然而，由于可靠性问题，例如产量低、均匀性差和耐久性问题，基于忆阻器阵列的系统在实现具有完全设备端学习的实时 AI 算法方面面临挑战。在这里，我们报告了一个基于无选择器模拟忆阻器阵列的模拟计算平台。我们使用具有逐渐氧分布的界面型氧化钛忆阻器，具有高可靠性、高线性度、无成型属性和自整流性。我们的平台由无选择器（单忆阻器）1 K（32 × 32）交叉开关阵列、外围电路和数字控制器组成，可以通过自校准在模拟域中运行 AI 算法，无需补偿操作或预训练。我们通过实时视频前景和背景分离来说明该系统的功能，实现了 30.49 dB 的平均峰值信噪比和 0.81 的结构相似性指数测量;这些值类似于理想情况下的模拟值。