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Tissue vs. Silicon: The Future of Deep Learning Hardware

If our brains processed information the same way today’s machine learning products consume computing power, you could fry an egg on your head. If you think about the brain like a circuit board that “lights up” when we need to process a thought, you’d see that only the neurons local to that specific thought would activate — not the entire brain. In machine learning computing, the entire “brain” is lighting up, which is incredibly inefficient — not to mention terrible for the environment. There’s got to be a better way. Instead of processing a petabyte of compute in a cell phone’s worth of memory (which is happening with today’s machine learning algorithms), we need to flip the script and process a petabyte’s worth of memory in a cell phone’s worth of compute power. Hear from Neural Magic’s CEO and an award-winning professor, Nir Shavit, about his what we can learn from his recent research in connectomics that we can apply to better today's machine learning hardware.