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Use green computing to solve energy consumption problems faced in the development of artificial intelligence

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Release: 2023-11-07 14:25:01
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The development of artificial intelligence has many directions, but due to the large amount of computing power required, this forces the demand for hardware equipment to rise sharply. The carbon footprint generated by hardware equipment during operation is very huge, and the emissions generated by the operation of a model even exceed that of a car. This astonishing number forces us to re-examine the development of artificial intelligence

Use green computing to solve energy consumption problems faced in the development of artificial intelligence

More carbon emissions will intensify the greenhouse effect and cause greater damage to the environment. However, a one-size-fits-all solution will also hinder the rapid development of artificial intelligence. Such epoch-making technological development has strong strategic significance, so there is a greater need for a technology that reduces energy consumption and carbon emissions without affecting computing power. Green computing provides solutions to the energy consumption problems faced by artificial intelligence.

Use green computing to solve energy consumption problems faced in the development of artificial intelligence

Green computing is divided into four parts, namely energy-saving AI, energy-saving computing system, green measurement indicators and sustainable AI applications. By calculating the energy consumption required in the model life cycle, controlling the model size, and monitoring the running time, the energy consumption generated in each stage can be reduced.

Use green computing to solve energy consumption problems faced in the development of artificial intelligence

Encourage the use of integrated learning methods to simplify the basic development process, thereby reducing the waste of resources caused by repeated development of the same content. In addition, sharing processes through cloud platforms can help the entire industry reduce the rate of double calculations. In addition to adjusting the training method, lightweighting the model is also an important idea. Developing smaller models with higher computing power can further reduce the cost of green computing and improve its feasibility for industry applications

Use green computing to solve energy consumption problems faced in the development of artificial intelligence

The development of green computing requires more than just research in the academic field. Enterprises should also be committed to sustainable development and proactively reduce emissions through process optimization. In addition, the government should play an important role as a facilitator to promote the popularity of green computing by implementing appropriate policies and requirements. An appropriate balance needs to be found between the development of artificial intelligence and environmental protection

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source:sohu.com
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