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Training a neural network in phase-change memory beats GPUs

2 monthes ago   |   By Ars Technica

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Compared to a typical CPU, a brain is remarkably energy-efficient, in part because it combines memory, communications, and processing in a single execution unit, the neuron. A brain also has lots of them, which lets it handle lots of tasks in parallel.
Attempts to run neural networks on traditional CPUs run up against these fundamental mismatches. Only a few things can be executed at a time, and shuffling data to memory is a slow process. As a result, neural networks have tended to be both computationally and energy intensive. A few years back, IBM announced a new processor design...
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