Energy is a massive expense for carriers, and new energy-focused technologies from energy efficiency to renewable energy, promise to reduce Telecom energy expenses while meeting sustainability goals, improving performance, uptime guarantees, and preparing for growing demand.
Silicon Valley, California, Aug 29, 2018/Meeting Recap/ Today, the Comtech Forum held a meeting on energy use in the telecommunications industry. We were hosted by Nvidia, and were treated to a tour of their Executive Briefing Center, where they demonstrated some amazing uses of GPU processing for AI, imaging, and computer vision. During their presentation, Nvidia also showed us how GPU processing could improve efficiency in datacenters, and how AI in the network could optimize the use of energy.
The day began with a few statistics from our own Derek Kerton, most notably the fact that telecoms is estimated to tally about 4% of the global energy use. Our crowd pondered this number, considering how to lower it. But the reality is that it will be hard to lower, because while telecoms is trying to reduce their energy demand, so, too, are all the other major users of energy, so the shares may remain similar. Furthermore, while other industries face more steady demand, telecoms are faced with constantly increasing data throughput, growing networks, device penetration, and IoT. So, it is a big challenge to meet all that telecom demand, and to merely keep energy use steady.
But there are double bottom-line benefits to energy efficiency. Once in reduced cost of electricity, and the other in the reduced costs of cooling that accompany reduced energy use. This means that network operators (and datacenter operators) have a bigger incentive to reduce energy use than other sectors.
As always, we had a host of startup or product companies pitching their solutions. There were a few dedicated to the same notion, of attaching sensors to fuse panels or circuits, and enabling consumers to more closely measure and monitor their overall and device-specific energy profiles. For all of these companies, the data would then be used to apply analytics to improve maintenance and reliability.