How can we design and build more electric-efficient generative AI and computers?

There are a number of ways to design and build more electric-efficient generative AI and computers. Here are some ideas:

1. Develop new model architectures that are more energy-efficient.

Generative AI models are becoming increasingly complex and capable, but this complexity comes at a cost in terms of energy consumption. Researchers are developing new model architectures that are more energy-efficient, such as SparseML and BrainChip. These architectures can reduce the energy consumption of generative AI models by up to 90%.

2. Develop new training algorithms that are more energy-efficient.

The training algorithms used to train generative AI models can also have a significant impact on their energy consumption. Researchers are developing new training algorithms that are more energy-efficient, such as the one used by BrainChip. These algorithms can reduce the energy consumption of generative AI models by reducing the number of computations that are required to train them.

3. Develop new hardware that is specifically designed for running generative AI models.

Current hardware, such as CPUs and GPUs, is not specifically designed for running generative AI models. This can lead to inefficiencies and waste energy. Researchers are developing new hardware that is specifically designed for running generative AI models, such as the Stratix 10 MX chip from Intel. This hardware can be much more energy-efficient than current hardware for running generative AI models.

4. Standardize the measurement and reporting of energy efficiency.

There is currently no standard way to measure or report the energy efficiency of generative AI models. This makes it difficult to compare the energy efficiency of different models and to track progress over time. Researchers and organizations are working to develop a standard way to measure and report the energy efficiency of generative AI models. This will make it easier to develop and deploy energy-efficient generative AI models in real-world applications.

5. Develop tools and resources to help people reduce the energy consumption of their generative AI models.

Many people who use generative AI models are not aware of the energy implications of their use. Additionally, there are limited resources available to help people reduce the energy consumption of their generative AI models. Researchers and organizations are working to develop tools and resources to help people reduce the energy consumption of their generative AI models. This will make it easier for people to use generative AI models in a more sustainable way.

6. Raise awareness of the energy implications of generative AI use.

Many people are not aware of the energy implications of generative AI use. It is important to raise awareness of this issue so that people can make informed decisions about how they use generative AI models. Researchers and organizations can help to raise awareness of this issue by publishing articles and blog posts, giving presentations, and developing educational materials.

7. Encourage the development of energy-efficient generative AI best practices.

Organizations can encourage the development of energy-efficient generative AI best practices by developing and implementing their own energy-efficient generative AI policies. Additionally, organizations can collaborate with researchers and other organizations to develop industry-wide energy-efficient generative AI best practices.

By taking these steps, we can design and build more electric-efficient generative AI and computers. This will make generative AI more accessible to a wider range of users and applications, and it will help to reduce the environmental impact of generative AI.