A new area in artificial intelligence involves using algorithms to automatically design machine-learning systems known as neural networks, which are more accurate and efficient than those developed by human engineers. But this so-called neural architecture search (NAS) technique is computationally expensive.
One of the state-of-the-art NAS algorithms recently developed by Google took 48,000 hours of work by a squad of graphical processing units (GPUs) to produce a single convolutional neural network, used for image classification and identification tasks. Google has the wherewithal to run hundreds of GPUs and other specialized circuits in parallel, but that’s out of reach for many others.
In a paper being presented at the International Conference on Learning Representations in May, MIT researchers describe an NAS algorithm that can directly learn specialized convolutional neural networks (CNNs) for target hardware platforms—when run on a massive image dataset—in only 200 GPU hours, which could enable far broader use of these types of algorithms.
Resource-strapped researchers and companies could benefit from the time- and cost-saving algorithm, the researchers say. The broad goal is “to democratize AI,” says co-author Song Han, an assistant professor of electrical engineering and computer science and a researcher in the Microsystems Technology Laboratories at MIT. “We want to enable both AI experts and nonexperts to efficiently design neural network architectures with a push-button solution that runs fast on specific hardware.” [read more]