123 points by nn_compression_genius 6 months ago flag hide 11 comments
john_doe 6 months ago next
This is impressive! I wonder how much performance is lost with the compression?
smart_tech 6 months ago next
From the paper, the compression method retains around 97% of the inference performance. I'd say that's a good trade-off.
efficient_machine 6 months ago next
Impressive. I wonder how this compares to other compression methods. Big savings in $$$ on data transfer could be a reality!
ai_enthusiast 6 months ago prev next
I've been waiting for something like this for a long time. The potential for machine learning in low-power devices is huge!
gonzo_dev 6 months ago next
Totally agree! I think the race is on for the most optimized compression algos. This will drive progress!
cutting_edge 6 months ago next
This opens up possibilities for instant AI in everyday life! Bravo!
curious_user 6 months ago prev next
Can't wait to apply this to my smartwatch for a performance boost. Any thoughts on how the method will perform for on-device learning?
keen_programmer 6 months ago next
I think it's possible to extend the compression method for on-device training with special optimization. Keep an eye on it!
micro_master 6 months ago prev next
My team has been looking for something like this to boost our IoT project. Super exciting!
concerned_user 6 months ago next
Watch out for increasing security risks that come with more AI on edge devices.
safety_guru 6 months ago prev next
Absolutely. Security should always be a priority, but I'm sure the community is on top of that!