The combination of the right software and commodity hardware will prove capable of handling most machine learning tasks

The Data Exchange Podcast: Nir Shavit on multicore software, neurobiology, and deep learning.

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In this episode of the Data Exchange I speak with Nir Shavit, Professor of EECS at MIT, and cofounder and CEO of Neural Magic, a startup that is creating software to enable deep neural networks to run on commodity CPUs (at GPU speeds or faster). Their initial products are focused on model inference, but they are also working on similar software for model training.

Ray Summit has been postponed until the Fall. In the meantime, enjoy an amazing series of virtual conferences beginning in mid May on the theme “Scalable machine learning, scalable Python, for everyone”. Go to for details.

Our conversation spanned many topics, including:

  • Neurobiology, in particular the combination of Nir’s research areas of multicore software and connectomics – a branch of neurobiology.
  • Why he believes the combination of the right software and CPUs will prove capable of handling many deep learning tasks.
  • Speed is not the only factor: the “unlimited memory” of CPUs are able to unlock larger problems and architectures.
  • Neural Magic’s initial offering is in inference, model training using CPUs is also on the horizon.

Related content:

Conferences :: Call For Speakers

A few SF Bay Area conferences I’m involved with are still looking for speakers:

  • Spark+AI Summit: deadline to submit is January 17th. The conference takes place at the Moscone Center in San Francisco and is shaping up to be the premier industry {Data + Machine Learning} conference series for the 2020 calendar year.
  • Apache Pulsar Summit: deadline to submit is January 31st. For more on Pulsar, see this Hacker News thread from January 2nd.
  • Ray Summit (“Scalable machine learning, Scalable Python, for everyone”): deadline to submit is January 24th. This is a new conference centered on Ray, an open-source system for scaling Python applications from single machines to large clusters. Based on what I know about the program, this is going to be an outstanding inaugural event.

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[Image by Pete Linforth from Pixabay]