Study finds new hardware offers faster computation for artificial intelligence with much less energy

Analogue deep learning, a new branch of artificial intelligence, promises faster computation with less energy consumption. The findings of the research were published in the journal ‘Science’. Programmable resistors are the key building blocks in analog deep learning, just like transistors are the core elements for digital processors. By repeating arrays of programmable resistors in complex layers, researchers can create a network of analogue artificial “neurons” and “synapses” that execute computations just like a digital neural network.This network can then be trained to achieve complex AI tasks like image recognition and natural language processing.