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Marvell at FMS 2019: NVMe Over Fabrics Controllers, AI On SSD
Taking things to the logical next step, Marvell also announced a native Ethernet/NVMeoF SSD controller. The 88SS5000 is effectively their 88SS1098 NVMe controller with the PCIe interface replaced by the dual 25GbE interface used by the NVMe to Ethernet converter. This new single-chip solution for Ethernet-attached SSDs helps cut costs and power consumption, making the whole idea more palatable to datacenter customers. Marvell showed samples of this controller paired with 8TB of Toshiba 96L 3D TLC NAND and 12GB of DDR4 DRAM.
Looking further into the future, Marvell shared their take on the idea of Computational Storage—SSDs that do more than just store data. Marvell is working to integrate a Machine Learning engine into future SSD controllers, allowing inferencing tasks to be offloaded from CPUs or GPUs onto the SSDs that already store the data being processed. The hardware setup is basically the same mess of cables connecting FPGAs to Flash that Marvell has shown in previous years, but on the software side their demo has matured greatly.
In addition to demonstrating realtime object recognition using a pre-trained model, Marvell now has a system to perform offline recognition on videos stored on the SSD. Their demo presented the results of this recognition as a graph showing which objects were recognized over the duration of a video. There was also a content-aware search engine that would return the segments of stored videos that depict the requested objects. For the demo, this functionality was exposed through a simple web interface. In production, the envisioned use case is to have an application server aggregating results from an array of content-aware SSDs that each perform some kind of analytics on their share of the overall dataset.
(Score: 1, Insightful) by Anonymous Coward on Friday August 16 2019, @10:25PM
That only that this would accelerate is the search for data that you don't know that you actually have.
I could see that being useful in things like meteorology and astrophysics, maybe voice recognition. But particularly I think surveillance.
Right? Because you know where you're shit is. You put it there. So why would you need to do a fuzzy search of images and audio, unless the search was being implemented by someone who doesn't have your prior knowledge? Except perhaps it will dumb down UI's even further so that people can say: "computer, show me that... er... You know, that thing I was looking at a while back.".
As if processing data for tards doesn't melt enough glaciers as it is.