Quantization And The Quest For More Efficient AI Modeling
That said, the next frontier for further reducing the computational resource requirements of on-device machine learning is being pioneered by Qualcomm, and it drives that math down to even less complex 4-bit integer operations , while maintaining a similar level of accuracy as the original floating point-derived model.
This is an example of a 540p pixel input resolution image being scaled up to 4K resolution, which results in much cleaner lines, sharper textures, and a better overall experience. Though Qualcomm has a non-algorithmic version of this available today,, someday in the future, mobile enthusiast gamers are going to be treated to even better levels of image quality without sacrificing battery life and with even higher frame rates.
This is just one example of gaming and media enhancement with pre-trained and quantized machine learning models, but you can quickly think of a myriad of applications that could benefit greatly, from recommendation engines to location-aware guidance, to computational photography techniques and more. We just needed a new math for all this AI heavy lifting on smartphones and other lower power edge devices, and it appears Qualcomm is
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