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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 that was a bit closer to a collection of neurons and could execute neural networks far more efficiently. But this didn’t help much with training the networks in the first place.
Now, IBM is back with a hardware design that’s specialized for training neural networks. And it does this in part by directly executing the training in a specialized type of memory.
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Source: Ars Technica – Training a neural network in phase-change memory beats GPUs