Accelerating Machine Intelligence – Project Juno AI

We've heard that it is prohibitively expensive for startups and academics to train machine learning models, and this is due to the rental or purchase costs of hardware. The results from one recent Google paper were estimated to cost $13k to emulate. That's just to reproduce the final model, not to emulate the whole experimentation and hyperparameter optimisation caboodle. Equally, there are intelligence tasks (training, inference, or prediction) that would ideally happen on the cellphone or remote sensor but are too compute constrained locally, so currently rely on uploading data to the cloud for processing. Machine intelligence is the future of computing, so what needs to happen at a hardware level to make it faster and more energy- and cost-efficient? We talked to Simon Knowles, CTO of Graphcore, about hardware acceleration of machine intelligence.