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Researchers Find A New Way To Make AI 1000X More Energy-Efficient

This method and system will not only enhance the performance and accuracy of the system but will also boost up the robustness, flexibility and reduce the carbon footprint of the machine
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While it comes with extreme benefits, artificial intelligence has time and again proved to be notoriously energy-consuming when implemented with traditional methods. According to data, training a single AI model can produce the same amount of CO2 as done by five cars in their whole span. In an attempt to create energy-efficient AI, researchers at UCL have created a system that can enhance the performance of a brain-inspired computing system by reducing the AI’s carbon footprint.

In a recent study, published in a paper, the researchers and engineers of UCL have suggested the application of ‘committee machines’ on the memristor-based neural networks. Physically implemented neural networks undergo issues like device faults, random telegraph noise, variability in device-to-device interaction, and line resistance. Thus, it was noted that the accuracy of the AI system could be vastly improved by replacing the transistors with memristors for all devices. Deploying simulations and experimental data from memristive devices would further allow them to enhance their performance without increasing the number of memristors.

With the ever-increasing demand for data, it can cause difficulties in increasing data transmission capacity after a certain point; thus, this study can be extremely beneficial for the current era. 

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