For deep learning and ML practitioners who are unfamiliar with this, the big deal about memristors is that they have memory and therefore produce different read

For deep learning and ML practitioners who are unfamiliar with this, the big deal about memristors is that they have memory and therefore produce different readout outputs for different input sequences over time -- out-of-the-box, without requiring any training. These researchers took a network of memristors, called the "reservoir," added a linear layer with a SoftMax on top of the reservoir, trained this hybrid network on a lower-resolution variant of MNIST (feeding pixel values over time, as varying voltages), and achieved classification accuracy superior to a tiny neural net despite having only 1/90th the number of neurons.[1] Note that they only trained the added layer; they did NOT have to train the reservoir. Figure (a) in this image has a simplified diagram of the reservoir + added layer architecture: https://www.nature.com/articles/s41467-017-02337-y/figures/1 https://www.nature.com/articles/s41467-017-02337-y/figures/1 -- only the matrix Θ had to be learned. The potential here, over time, is for having highly scalable hardware components that can be plugged into neural net architectures as and where needed for learning to recognize and work with sequences out of the box. PS. For clarity's sake, I'm ignoring a lot of important details and playing fast and loose with language. If you're really curious about this, I highly recommend you read at least the abstract and introduction of the Nature paper, which is well-written and straightforward to follow. [1] https://news.engin.umich.edu/2017/12/new-quick-learning-neural-network-powered-by-memristors/ https://news.engin.umich.edu/2017/12/new-quick-learning-neur...