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Offloading ML Processing to Storage Devices with NGD Systems | Utilizing AI 2×28

Today’s storage devices (disks and SSDs) have processors and memory already, and this is the concept of computational storage. If drives can process data locally, they can relieve the burden of communication and processing and help reduce the amount of data that gets to the CPU or GPU. In this episode, Vladimir Alves and Scott Shadley join Chris Grundemann and Stephen Foskett to discuss the AI implications of computational storage. Modern SSDs already process data, including encryption and compression, and they are increasingly taking on applications like machine learning. Just as industrial IoT and edge computing are taking on ML processing, so too are storage devices. Current applications for ML on computational storage include local processing of images and video for recognition and language processing, but these devices might even be able to execute ML training locally as in the case of federated learning.

Three Questions

  1. Are there any jobs that will be completely eliminated by AI in the next five years?
  2. Can you think of any fields that have not yet been touched by AI?
  3. How small can ML get? Will we have ML-powered household appliances? Toys? Disposable devices?

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About the author

Stephen Foskett

Stephen Foskett is an active participant in the world of enterprise information technology, currently focusing on enterprise storage, server virtualization, networking, and cloud computing. He organizes the popular Tech Field Day event series for Gestalt IT and runs Foskett Services. A long-time voice in the storage industry, Stephen has authored numerous articles for industry publications, and is a popular presenter at industry events. He can be found online at,, and on Twitter at @SFoskett.

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