BY LEVERAGING SPECIALISED ARCHITECTURES AND EMBRACING INNOVATIVE SOFTWARE DESIGN , UAE ORGANISATIONS CAN SIGNIFICANTLY LOWER ENERGY DEMANDS ASSOCIATED WITH AI APPLICATIONS .
CASE STUDY
As Artificial Intelligence , AI applications multiply across industries , the drive for energy efficiency has become increasingly critical . The energy consumption of data centres hosting ever-growing AI workloads has risen sharply in recent years , contributing significantly to global energy use and emissions .
A McKinsey report published in September 2024 predicts that the US data centre power needs , excluding cryptocurrency are to experience a 3x growth by the end of the decade . Similar skyrocketing demands are seen in other parts of the world as well .
But as well as consuming energy , AI-driven solutions could hold the key to resolving one of the most fundamental questions of our age – how can we keep developing and utilising powerful AI models while still moving towards carbon-free , sustainable economies ?
Research currently being undertaken at Mohamed bin Zayed University of Artificial Intelligence , MBZUAI takes a multi-pronged approach toward this challenge .
One area of focus is Graphics Processing Units , GPUs and Tensor Processing Units , TPUs . Traditional computing architectures such as CPUs are not always the most efficient for AI tasks , so GPUs and TPUs have emerged as specialised tools designed specifically for the parallel processing requirements of AI .
The basic unit of information in all computation is a bit , short for binary digit , with each bit only having one of two values – a 1 or a 0 . Finding a way to conserve the energy used to store , retrieve , and manipulate the 1s and 0s used in computations would cut wasted energy , reduce heat loss , speed up processing , and increase energy efficiency .
In fact , removing potentially unnecessary bits in expensive computations , via quantisation or pruning can significantly aid in addressing the energyefficiency crisis facing the hardware landscape . The co-design challenge is ensuring you can rework the hardware to be more efficient and still do the computations you need accurately .
As transistors continue to scale and become smaller , an important consideration is to ensure their reliability in the face of errors . Errors in hardware have been a thorn in the side of large-scale data centre companies such as Google and Meta .
These architectures are the building blocks of AI , with tens of thousands at work in data centres right now , and many more required for the new generation of data centres currently in development . To continue fuelling the AI revolution , all these
BY LEVERAGING SPECIALISED ARCHITECTURES AND EMBRACING INNOVATIVE SOFTWARE DESIGN , UAE ORGANISATIONS CAN SIGNIFICANTLY LOWER ENERGY DEMANDS ASSOCIATED WITH AI APPLICATIONS .
Yet building reliable processors can not only address energy efficiency but can further help to build a sustainable future . The longer a manufactured processor can be utilised in a data centre , because of its reliability , the lower its carbon footprint due to the huge upfront cost of building these processors .
Field-Programmable Gate Arrays , FPGAs offer another alternative , allowing for customisable hardware solutions tailored to specific AI tasks . By enabling developers to optimise circuits for applications , FPGAs can significantly reduce energy consumption while maintaining high performance .
MBZUAI is looking at ways to reduce waste and deploy resources more efficiently in the upper layers , building sustainability in the development and application of AI models . System software used for large language models , for training , to build the models and inference , and to use them , needs to work closely with the hardware design to achieve better energy efficiency . devices must conserve and limit energy usage , increasing processor performance with the minimal number of Joules possible .
Hardware specialisation holds the key . By manufacturing specialised AI hardware pipelines such as in GPUs or TPUs to be energy efficient , it will be possible to increase the energy efficiency of data centres at scale , even as they manage an ever-growing volume and complexity of calculations .
The aim is to develop individual components in a co-designed way , so that energy consumption is reduced at the hardware level without impacting software performance . The more you investigate architecture optimisation , the more opportunities present themselves at the most fundamental levels .
Two lines in the current systems research done by MBZUAI researchers to this end strive toward AI sustainability . One is to improve the distributed model training through aggressive overlapping operators using heterogeneous resources within a GPU server , such as compute-intensive matrix multiplication ones with network-intensive communication ones . This way , both overall performance and resource utilisation can be improved .
The other is to reduce the amount of computation in inference . Potential solutions in this direction
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