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Brain-Inspired Hardware and Algorithm Co-Design with Melika Payvand - #585
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Adapting Line Learning Style Algorithms to Hardware?
An min learning is really calt problem. It's not like you can normally, when you want to go and train an ear on that work, you just down loe this data set and just run it through yournobr. So info ation has to be locally available in time. The other challenge is that, for example, back propegation, which is the basically workhorse of all training, all around networks, it requires information from all the senapses. And if you want to build that in hardware, you're going to blow up this entire system with wires.
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