Data-Pattern Enabled Self-Recovery Low-Power Storage System for Big Video Data
The growing popularity of powerful mobile devices such as smart phones and tablet devices has resulted in the exponential growth of demand for video applications. However, due to the large video data size and intensive computation, mobile video applications require frequent embedded memory access, which consumes a large amount of power and limits battery life. In this paper, we present a low-cost self-recovery video storage system by investigating meaningful data patterns hidden in big video data, by introducing data mining techniques to the hardware design process. We propose a two-dimensional data-pattern approach to explore horizontal data-association and vertical data-correlation characteristics. Such data relationship discovery and pattern identification enable a new dimension for the hardware design space and bring self-recovery ability to memories in the presence of bitcell failures. Based on the identified optimal data patterns, we present a low-cost and efficient SRAM design to enable data self-recovery at low voltages. A 45nm 32kb SRAM is implemented that delivers good video quality at near-threshold voltage (0.5 V) with negligible area overhead (7.94%).
videos data mining data pattern low-power self-recovery on-chip memory