AMD says to accomplish this, they will require to increase the energy efficiency of a compute node at a rate more than 2.5x faster than the aggregate industry-wide improvement made during the last five years.
AMD explains that accelerated compute nodes are the most powerful and advanced computing systems in the world used for scientific research and large-scale supercomputer simulations.
They provide the computing capability used by scientists to achieve breakthroughs across many fields including material sciences, climate predictions, genomics, drug discovery and alternative energy.
It is also integral for training AI neural networks that are used for activities including speech recognition, language translation, and expert recommendation systems, with similar promising uses in the next decade.
The 30x goal would save billions of kilowatt hours of electricity in 2025, reducing the power required for these systems to complete a single calculation by 97% over five years.
“We are now setting a new goal for modern compute nodes using our high-performance CPUs and accelerators when applied to AI training and high-performance computing deployments,” says AMD executive vice president and chief technological officer Mark Papermaster.
“AMD’s 30x goal outpaces industry energy efficiency performance in these areas by 150% compared to the previous five-year time period,” Papermaster claims.
“AMD has taken a bold position on the energy efficiency of its processors, this time for the accelerated compute for AI and high performance computing applications,” notes Intersect360 CEO Addison Snell.
“Future gains are more difficult now as the historical advantages that come with Moore’s Law have greatly diminished. A 30-times improvement in energy efficiency in five years will be an impressive technical achievement that will demonstrate the strength of AMD technology and their emphasis on environmental sustainability,” Snell emphasises.
Increased energy efficiency for accelerated computing applications is part of the company’s new goals in environmental, social, governance (ESG) spanning its operations, supply chain and products.
In addition to compute node performance/Watt measurements, to make the goal particularly relevant to worldwide energy use, AMD uses segment-specific data centre power utilisation effectiveness (PUE) with equipment utilisation taken into account.
The energy consumption baseline uses the same industry energy per operation improvement rates as from 2015-2020, extrapolated to 2025. The measure of energy per operation improvement in each segment from 2020-2025 is weighted by the projected worldwide volumes multiplied by the Typical Energy Consumption (TEC) of each computing segment to arrive at a meaningful metric of actual energy usage improvement worldwide, according to AMD.
“The energy efficiency goal set by AMD for accelerated compute nodes used for AI training and high performance computing fully reflects modern workloads, representative operating behaviours and accurate benchmarking methodology,” concludes Koomey Analytics president Dr Jonathan Koomey.