The growing energy use of AI has gotten a lot of people working on ways to make it less power hungry. One option is to develop processors that are a better match to the sort of computational needs of neural networks, which require many trips to memory and a lot of communication between artificial neurons that might not necessarily reside on the same processor. Termed “neuromorphic” processors, this alternative approach to hardware tends to have lots of small, dedicated processing units with their own memory and an extensive internal network connecting them.
Examples like Intel’s Loihi chips tend to get competitive performance out of far lower clock speeds and energy use, but they require a lot of silicon to do so. Other options give up on silicon entirely and perform the relevant computation in a form of phase change memory.
A paper published in Nature on Wednesday describes a way to get plain-old silicon transistors to behave a lot like an actual neuron. And unlike the dedicated processors made so far, it only requires two transistors to do so.
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This articles is written by : Nermeen Nabil Khear Abdelmalak
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