They become analog computations instead of digital

Thursday, August 1st, 2019

University of Michigan engineers are claiming the first memristor-based programmable computer for AI that can work on all its own.

“Memory is really the bottleneck,” says University of Michigan professor Wei Lu. “Machine learning models are getting larger and larger, and we don’t have enough on-chip memory to store the weights.” Going off-chip for data, to DRAM, say, can take 100 times as much computing time and energy. Even if you do have everything you need stored in on-chip memory, moving it back and forth to the computing core also takes too much time and energy, he says. “Instead, you do the computing in the memory.”

His lab has been working with memristors (also called resistive RAM, or RRAM), which store data as resistance, for more than a decade and has demonstrated the mechanics of their potential to efficiently perform AI computations such as the multiply-and-accumulate operations at the heart of deep learning. Arrays of memristors can do these tasks efficiently because they become analog computations instead of digital.

The new chip combines an array of 5,832 memristors with an OpenRISC processor, 486 specially-designed digital-to-analog converters, 162 analog-to-digital converters, and two mixed-signal interfaces act as translators between the memristors’ analog computations and the main processor.

Scientists created the first memristor 11 years ago and foresaw their use in neural nets.

Comments

  1. Sam J. says:

    HP has been advertising their “great” memristors tech for…a long time but best as I can tell they have not been able to capitalize on it.

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