AI chips could get a sense of time | ScienceDaily

The first memristor with a 'relaxation time' that can be tuned is reported today in Nature Electronics, in a study led by the University of Michigan.

The problem is that GPUs operate very differently from the artificial neural networks that run the AI algorithms -- the whole network and all its interactions must be sequentially loaded from the external memory, which consumes both time and energy. In contrast, memristors offer energy savings because they mimic key aspects of the way that both artificial and biological neural networks function without external memory. To an extent, the memristor network can embody the artificial neural network.

Memristors operate a little differently. Rather than the total presence or absence of a signal, what changes is how much of the electrical signal gets through. Exposure to a signal reduces the resistance of the memristor, allowing more of the next signal to pass. In memristors, relaxation means that the resistance rises again over time.

Heron calls this type of oxide, an entropy-stabilized oxide, the"kitchen sink of the atomic world" -- the more elements they add, the more stable it becomes. By changing the ratios of these oxides, the team achieved time constants ranging from 159 to 278 nanoseconds, or trillionths of a second. The simple memristor network they built learned to recognize the sounds of the numbers zero to nine. Once trained, it could identify each number before the audio input was complete.

The research was funded by the National Science Foundation. It was done in partnership with researchers at the University of Oklahoma, Cornell University and Pennsylvania State University.

Source: Tech Daily Report (techdailyreport.net)

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