[3S4-GS-2] Machine learning:

[3S4-GS-2-01] Interpolation of HDD parameters with neural network model

Improving the linear interploation accuracy with a differentiable linear interpolation layer

〇Katsuya Sugawara¹, Yousuke Isowaki¹, Kenichiro Yamada¹, Naoki Tagami², Takeyori Hara² (1. TOSHIBA CORPORATION, 2. Toshiba Electronic Devices & Storage Corporation)

Keywords:Neural network, Linear interpolation, Differentiable

In low-cost embedded systems or real-time processing required fields, linear interpolation is still used due to resource or processing time restriction. HDD is one of an embedded system which requires real-time processing, thus using linear interpolation for parameters. To improve linear interpolation accuracy, we developed a small neural network model with a differentiable linear interpolation layer. Neural network was optimized by the output of attached differentiable linear interpolation layer to obtain better fittings with linear interpolation. Neural network outputs are modulated as better inputs for linear interpolation and can be used as alternatives of linear interpolation implemented in embedded systems as it is. We have tested out model with RRO (Repeatable Run Out) correction data, an HDD parameter, and obtained 10% of linear interpolation accuracy improvement or 7.5% of sampling rate reduction which leads to reduced inspection time.