Google DeepMind's 'Sideways' takes a page from computer architecture ZDNet

Increasingly, machine learning forms of artificial intelligence are contending with the limits of computing hardware, and it's causing scientists to rethink how they design neural networks. That was clear in last week's research offering from Google, called Reformer, which aimed to stuff a natural language program into a single graphics processing chip instead of eight. And this week brought another offering from Google focused on efficiency, something called Sideways. With this invention, scientists have borrowed a page from computer architecture, creating a pipeline that gets more work done at every moment. Most machine learning neural nets during their training phase use a forward pass, a transmission of a signal through layers of the network, followed by backpropagation, a backward pass through the same layers, only in reverse, to gradually modify the weights of a neural network till they're just right.