Artificial Intelligence (AI) and machine learning (ML) require significant computing power and storage capacity. And because increasingly complex AI and ML models require more data to be trained effectively, the need for powerful computing resources to train the models only grows. Organizations and individuals using – or planning to adopt – AI and machine learning into their systems must address this problem.
Here are three ways to tackle the increasing powers needs of AI and ML:
1: Use of cloud computing services such as Amazon Web Services, Microsoft Azure or Google Cloud Platform. These services allow organizations to easily rent computing power and storage capacity on-demand, rather than having to invest in and maintain their own physical infrastructure. By only renting the amount of computing power and storage capacity that is needed for a given workload, organizations can avoid overspending on resources that are not fully utilized.
2: Software optimization, such as model compression and pruning, can be used to reduce the computational requirements of AI models, making them more efficient and easier to run on less powerful hardware. Model compression aims at reducing the size of the model by removing unnecessary parameters or by reducing the precision of the parameters, resulting in a lighter model that consumes less memory and computational resources. Pruning aims at removing the unimportant weights or neurons of the model, making the model less complex and more efficient in terms of computational resources. By using these techniques, AI models can be made more efficient and run on less powerful hardware.
3: Another solution is to use specialized hardware designed specifically for AI workloads, such as graphics processing units (GPUs) and tensor processing units (TPUs). These types of hardware are optimized for the matrix and vector operations that are commonly used in deep learning and can provide significantly faster performance than traditional CPUs. They consume less power per computation operation compared to CPUs, that makes them more energy efficient.
By implementing a combination of these approaches, organizations can take a more efficient and cost-effective approach to addressing the problem of computing power needs in AI.