Samsung Advanced Institute of Technology (SAIT) this week announced that they have successfully developed On-Device AI lightweight technology that performs computations 8 times faster than the existing 32-bit deep learning data for servers.
By adjusting the data into groups of under 4 bits while maintaining accurate data recognition, this method of deep learning algorithm processing is simultaneously much faster and much more energy efficient than existing solutions.
Samsung’s new On-Device AI processing technology determines the intervals of the significant data that influence overall deep learning performance through ‘learning’. This ‘Quantization1 Interval Learning (QIL)’ retains data accuracy by re-organizing the data to be presented in bits smaller than their existing size. SAIT ran experiments that successfully demonstrated how the quantization of an in-server deep learning algorithm in 32 bit intervals provided higher accuracy than other existing solutions when computed into levels of less than 4 bits.
As this system therefore requires less hardware and less electricity, it can be mounted directly in-device at the place where the data for an image or fingerprint sensor is being obtained, ahead of transmitting the processed data on to the necessary end points.
On-Device AI technology can reduce the cost of cloud construction for AI operations since it operates on its own and provides quick and stable performance for use cases such as virtual reality and autonomous driving. Furthermore, On-Device AI technology can save personal biometric information used for device authentication, such as fingerprint, iris and face scans, onto mobile devices safely.