  Machine learning has been getting a large attention in the recent years, as a
tool to process big data generated by ubiquitous sensors in our daily life.
High speed, low energy computing machines are in demand to enable real-time
artificial intelligence at the edge, i.e., without the support of a remote
frame server in the cloud. Such requirements challenge the complementary
metal-oxide-semiconductor (CMOS) technology, which is limited by the 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 a
crosspoint resistive memory circuit with feedback configuration can execute
linear regression and logistic regression in just one step by computing the
pseudoinverse matrix of the data within the memory. The most elementary
learning operation, that is the regression of a sequence of data and the
classification of a set of data, can thus be executed in one single
computational step by the novel technology. One-step learning is further
supported by simulations of the prediction of the cost of a house in Boston and
the training of a 2-layer neural network for MNIST digit recognition. The
results are all obtained in one computational step, thanks to the physical,
parallel, and analog computing within the crosspoint array.
