IBM’s new chip is designed to do both high-precision learning and low-precision inference across the three main flavors of deep learning
The field of deep learning is still in flux, but some things have started to settle out. In particular, experts recognize that neural nets can get a lot of computation done with little energy if a chip approximates an answer using low-precision math. That’s especially useful in mobile and other power-constrained devices. But some tasks, especially training a neural net to do something, still need precision. IBM recently revealed its newest solution, still a prototype, at the IEEE VLSI Symposia: a chip that does both equally well.
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