Singapore’s rapid adoption of AI technologies is driving significant advancements across various sectors, from healthcare to urban planning. However, this surge in AI usage also brings about increased energy consumption, posing a challenge to sustainability goals. As a global leader in AI innovation, Singapore is committed to balancing technological progress with environmental stewardship.

Many enterprises are focused on sustainability and have committed to becoming carbon neutral by 2050 to align with the UN’s net-zero goal. To meet that target and stave off the worst effects of climate change, organisations must reduce energy use, increase reliance on renewable and carbon-free energy sources, find ways of sequestering carbon or capturing it before it escapes into the atmosphere, and figure out how to reuse resources and eliminate waste. In short, a holistic approach is necessary to achieve sustainable AI.

Singapore is leveraging innovative strategies such as geo-distributed workloads and digital twins to optimise energy use and reduce carbon emissions. The Infocomm Media Development Authority (IMDA) has been actively promoting green AI initiatives, ensuring that the nation’s digital expansion aligns with sustainability objectives. By adopting energy-efficient AI systems and exploring renewable energy sources, Singapore aims to set an example for sustainable growth in the digital age.

Four strategies for more energy-efficient AI

Researchers are currently investigating how resources can be used efficiently, whether AI workloads happen on the cloud, supercomputers, or on-premises data centers. Different types of resources can run certain workloads more efficiently than others. If an organisation needs to run certain types of workloads, how should those workflows be broken up? Some workloads may be best suited for the cloud, some may be best suited for a supercomputer, or an on-premises data center.

Analog accelerators

For decades, digital circuits have been the preferred option. They’re fast, powerful, and enable processing enormous amounts of data in record time. But with widespread AI use, those digital circuits have a limitation: they need massive amounts of power to operate. But as the saying goes: what’s old is new again. Analog circuits are comprised of components like resistors, capacitors, and inductors that operate in the analog domain and are a promising alternative to reduce energy consumption.

Instead of using a binary system of zeros and ones, analog circuits replace binary logic with a range of continuous signals. When constructed with components like memristors that can store data, data movement from memory to the accelerator is reduced, which reduces energy consumption. Special-purpose accelerators that target specific workloads are generally more efficient than general-purpose ones. This different approach achieves the same goal, but it can yield significant improvement in energy consumption at the chip level.

Digital twins

A virtual representation of a physical system, like a cloud, supercomputer, or on-premises data center. This virtual representation stays up-to-date and can be used for real-time optimisation of the physical system. Digital twins are generally classified according to how they interact with the physical system. The most elemental form of a digital twin is a simulation of the physical system, an example is a simulation of the cooling infrastructure in a data center used in the design of layout of the facility.

These types of “twins” have been used for decades in engineering. If the simulation exchanges data with the physical system to remain current, for example, by sensing the power consumption of the physical components in a data center and updating the model accordingly, it can evolve with the physical system and can be used to, among other things, maintain the operational efficiency of the system.

Geo-distributed workloads

Energy and water availability vary and are hyperlocal. HPE Labs, in collaboration with Colorado State University, has developed a set of optimization algorithms that examine the variation in carbon intensity, water availability, and energy cost in certain areas. It then uses that data to determine the best location to run a workload, like generative AI, around the globe to optimise resource usage hyperlocally and create significant savings in power and water consumption while reducing costs.

Leveraging waste heat

100 percent of electrical energy that goes into a data center ultimately gets converted to heat, which must be cooled and transferred. Data centers have traditionally used air cooling, but it is less efficient and more expensive than direct liquid cooling (DLC), which has grown in favor in the age of AI. DLC is a cooling method where liquid is pumped directly into a server to absorb heat emitted by all the systems components, including processors, GPUs, and memory, and then sent to a heat exchange system outside a data center.

It cools more efficiently, since water has four times more heat capacity than air. Liquid is also easier to contain and transport while minimising heat loss more efficiently, improving waste heat utilisation. Capturing waste heat is important because it can be used for other purposes, such as warming greenhouses to create ideal conditions for growing tomatoes or heating buildings.

As AI grows in popularity, it is especially reshaping fast-growing digital economies like Singapore and those across Asia. The region’s leadership in technology adoption comes with a responsibility to drive sustainable innovation. Meeting the surging energy demands of AI while advancing decarbonisation goals will require a fundamental shift in how we think about computing infrastructure. By embracing novel approaches, countries like Singapore can lead the charge in reimagining energy use for a more resilient, AI-powered future.

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