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MILPITAS, Calif., May 6, 2024 /PRNewswire-PRWeb/ -- The apocryphal tale of Benjamin Franklin venturing out into a storm with kite and key in hand set the stage for the 21st century's voracious appetite for electricity. Artificial Intelligence has proved to be a glutton for energy consumption. The amount of computing power necessary for AI machine learning is doubling every six months. (1) Data center behemoths like Amazon Web Services, Microsoft, and Google are looking to reduce their carbon footprint, increase efficiency, and reduce energy costs. To comply with this trend, the IT infrastructure is evolving towards a more distributed compute architecture and a broader variety of processor cores and types (such as Arm-based CPUs and system-on-chip processors). JB Baker, VP of Products for ScaleFlux says, "Large-scale AI workloads rely heavily on memory and networking, putting much of the current focus on alleviating bottlenecks in these areas. However, with the petabyte and exabyte scale of the datasets used in AI, power and efficiencies of the underlying storage infrastructure are quietly a major challenge. Improving the pipeline between storage, memory, and GPUs with solutions like solid-state drives (SSDs) enhanced with computational storage capabilities is critical in enabling companies to achieve their efficiency and energy sustainability goals."