3 ms·

Realtime capable hardware and software is all super intersting. It makes up for a very different challenge when building that something that has to fulfill a ce

Realtime capable hardware and software is all super intersting. It makes up for a very different challenge when building that something that has to fulfill a certain task in a certain amount of time instead of just providing a best effort solution. Therefore I'm always curious to learn about products and solutions in that field. However for this presentation I'm a bit puzzled by some of the marketing slides: - Optional NEON accelerates ML. - Advanced machine learning support Is that really a use-case for those processors? Can ML be hard-realtime anyway? I feel like a promotion that one could use those SIMD instructions for e.g. erasure-coding on storage use-cases or e.g. for signal processing on Telco/Audio/Video use-cases would be much closer to typical applications for such a CPU. Is it just me or sounds the "ML" on these slides more like "we need to join the hype" than an actual use-case? - Optional MMU enables Linux and cloud-native software development I agree - MMU is great, Linux support too. But what is cloud-native again? And is something that runs on a SSD controller in a datacenter now cloud native?

nl 6y ago
> Can ML be hard-realtime anyway? Sure, why not? The number of instructions needed to run inference on a given neural network is known so I don't see any real issues here. (Training is of course a different matter) > Is it just me or sounds the "ML" on these slides more like "we need to join the hype" than an actual use-case? You could build query-by-example indexes (for text, sound, images or videos) in the storage hardware with this kind of capability. That'd be pretty interesting. Imagine something like Isilon, but instead of allowing Hadoop analytics on the storage hardware it could do approximate nearest neighbour across images. (This is actually a pretty good idea...)
- wtallis 6y ago
  > You could build query-by-example indexes (for text, sound, images or videos) in the storage hardware with this kind of capability. That'd be pretty interesting. I've seen demos of this kind of thing from companies like NGD Systems, who use Cortex A53s in addition to realtime cores in their SSD controller. Part of the selling point for offloading this from the CPU is that code running on the SSD controller has much faster access to the dataset and can query it more efficiently, unconstrained by the drive's host interface. There are also more mundane use cases for ML inside a SSD as heuristics to predict data access patterns or media wearout trends, which can influence how the SSD chooses to store data and tackle error correction.

nl 6y ago

> Can ML be hard-realtime anyway? Sure, why not? The number of instructions needed to run inference on a given neural network is known so I don't see any real issues here. (Training is of course a different matter) > Is it just me or sounds the "ML" on these slides more like "we need to join the hype" than an actual use-case? You could build query-by-example indexes (for text, sound, images or videos) in the storage hardware with this kind of capability. That'd be pretty interesting. Imagine something like Isilon, but instead of allowing Hadoop analytics on the storage hardware it could do approximate nearest neighbour across images. (This is actually a pretty good idea...)

wtallis 6y ago
> You could build query-by-example indexes (for text, sound, images or videos) in the storage hardware with this kind of capability. That'd be pretty interesting. I've seen demos of this kind of thing from companies like NGD Systems, who use Cortex A53s in addition to realtime cores in their SSD controller. Part of the selling point for offloading this from the CPU is that code running on the SSD controller has much faster access to the dataset and can query it more efficiently, unconstrained by the drive's host interface. There are also more mundane use cases for ML inside a SSD as heuristics to predict data access patterns or media wearout trends, which can influence how the SSD chooses to store data and tackle error correction.