Sun Zhong, Pedretti Giacomo, Bricalli Alessandro, Ielmini Daniele
Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano and IU.NET, Piazza L. da Vinci 32, 20133 Milano, Italy.
Sci Adv. 2020 Jan 31;6(5):eaay2378. doi: 10.1126/sciadv.aay2378. eCollection 2020 Jan.
Machine learning has been getting attention in recent years as a tool to process big data generated by the ubiquitous sensors used in daily life. High-speed, low-energy computing machines are in demand to enable real-time artificial intelligence processing of such data. These requirements challenge the current metal-oxide-semiconductor technology, which is limited by Moore's law approaching its end and the communication bottleneck in conventional computing architecture. Novel computing concepts, architectures, and devices are thus strongly needed to accelerate data-intensive applications. Here, we show that a cross-point resistive memory circuit with feedback configuration can train traditional machine learning algorithms such as linear regression and logistic regression in just one step by computing the pseudoinverse matrix of the data within the memory. One-step learning is further supported by simulations of the prediction of housing price in Boston and the training of a two-layer neural network for MNIST digit recognition.
近年来，机器学习作为一种处理日常生活中无处不在的传感器所产生的大数据的工具而备受关注。需要高速、低能耗的计算机器来实现对此类数据的实时人工智能处理。这些需求对当前的金属氧化物半导体技术构成了挑战，该技术受到摩尔定律接近极限以及传统计算架构中通信瓶颈的限制。因此，迫切需要新颖的计算概念、架构和设备来加速数据密集型应用。在此，我们表明，具有反馈配置的交叉点电阻式存储电路可以通过计算存储器内数据的伪逆矩阵，在一步之内训练传统的机器学习算法，如线性回归和逻辑回归。对波士顿房价预测的模拟以及用于MNIST数字识别的两层神经网络的训练进一步支持了一步学习。
